prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. How big is the difference in performance between proposed model and baselines?
Context 1: Introduction
Attempts toward constructing human-like dialogue agents have met significant difficulties, such as maintaining conversation consistency BIBREF0  This is largely due to inabilities of dialogue agents to engage the user emotionally because of an inconsistent personality BIBREF1  Many agents use personality models that attempt to map personality attributes into lower dimensional spaces (e g  the Big Five BIBREF2)  However, these represent human personality at a very high-level and lack depth  They prohibit the ability to link specific and detailed personality traits to characters, and to construct large datasets where dialogue is traceable back to these traits 
For this reason, we propose Human Level Attributes (HLAs), which we define as characteristics of fictional characters representative of their profile and identity  We base HLAs on tropes collected from TV Tropes BIBREF3, which are determined by viewers' impressions of the characters  See Figure FIGREF1 for an example  Based on the hypothesis that profile and identity contribute effectively to language style BIBREF4, we propose that modeling conversation with HLAs is a means for constructing a dialogue agent with stable human-like characteristics  By collecting dialogues from a variety of characters along with this HLA information, we present a novel labelling of this dialogue data where it is traceable back to both its context and associated human-like qualities 
We also propose a system called ALOHA (Artificial Learning On Human Attributes) as a novel method of incorporating HLAs into dialogue agents  ALOHA maps characters to a latent space based on their HLAs, determines which are most similar in profile and identity, and recovers language styles of specific characters  We test the performance of ALOHA in character language style recovery against four baselines, demonstrating outperformance and system stability  We also run a human evaluation supporting our results  Our major contributions are: (1) We propose HLAs as personality aspects of fictional characters from the audience's perspective based on tropes; (2) We provide a large dialogue dataset traceable back to both its context and associated human-like attributes; (3) We propose a system called ALOHA that is able to recommend responses linked to specific characters  We demonstrate that ALOHA, combined with the proposed dataset, outperforms baselines  ALOHA also shows stable performance regardless of the character's identity, genre of the show, and context of the dialogue  We plan to release all of ALOHA's data and code 
Related Work
Task completion chatbots (TCC), or task-oriented chatbots, are dialogue agents used to fulfill specific purposes, such as helping customers book airline tickets, or a government inquiry system  Examples include the AIML based chatbot BIBREF5 and DIVA Framework BIBREF6  While TCC are low cost, easily configurable, and readily available, they are restricted to working well for particular domains and tasks 
Open-domain chatbots are more generic dialogue systems  An example is the Poly-encoder from BIBREF7 humeau2019real  It outperforms the Bi-encoder BIBREF8, BIBREF9 and matches the performance of the Cross-encoder BIBREF10, BIBREF11 while maintaining reasonable computation time  It performs strongly on downstream language understanding tasks involving pairwise comparisons, and demonstrates state-of-the-art results on the ConvAI2 challenge BIBREF12  Feed Yourself BIBREF13 is an open-domain dialogue agent with a self-feeding model  When the conversation goes well, the dialogue becomes part of the training data, and when the conversation does not, the agent asks for feedback  Lastly, Kvmemnn BIBREF14 is a key-value memory network with a knowledge base that uses a key-value retrieval mechanism to train over multiple domains simultaneously  We use all three of these models as baselines for comparison  While these can handle a greater variety of tasks, they do not respond with text that aligns with particular human-like characteristics 
BIBREF15 li2016persona defines persona (composite of elements of identity) as a possible solution at the word level, using backpropagation to align responses via word embeddings  BIBREF16 bartl2017retrieval uses sentence embeddings and a retrieval model to achieve higher accuracy on dialogue context  BIBREF17 liu2019emotion applies emotion states of sentences as encodings to select appropriate responses  BIBREF18 pichl2018alquist uses knowledge aggregation and hierarchy of sub-dialogues for high user engagement  However, these agents represent personality at a high-level and lack detailed human qualities  LIGHT BIBREF19 models adventure game characters' dialogues, actions, and emotions  It focuses on the agent identities (e g  thief, king, servant) which includes limited information on realistic human behaviours  BIBREF20 pasunuru2018game models online soccer games as dynamic visual context  BIBREF21 wang2016learning models user dialogue to complete tasks involving certain configurations of blocks  BIBREF22 antol2015vqa models open-ended questions, but is limited to visual contexts  BIBREF23 bordes2016learning tracks user dialogues but is goal-oriented  BIBREF24 ilinykh2019meetup tracks players' dialogues and movements in a visual environment, and is grounded on navigation tasks  All of these perform well in their respective fictional environments, but are not a strong representation of human dialogue in reality 
Methodology ::: Human Level Attributes (HLA)
We collect HLA data from TV Tropes BIBREF3, a knowledge-based website dedicated to pop culture, containing information on a plethora of characters from a variety of sources  Similar to Wikipedia, its content is provided and edited collaboratively by a massive user-base  These attributes are determined by human viewers and their impressions of the characters, and are correlated with human-like characteristics  We believe that TV Tropes is better for our purpose of fictional character modeling than data sources used in works such as BIBREF25 shuster2019engaging because TV Tropes' content providers are rewarded for correctly providing content through community acknowledgement 
TV Tropes defines tropes as attributes of storytelling that the audience recognizes and understands  We use tropes as HLAs to calculate correlations with specific target characters  We collect data from numerous characters from a variety of TV shows, movies, and anime  We filter and keep characters with at least five HLA, as those with fewer are not complex enough to be correctly modeled due to reasons such as lack of data  We end up eliminating 5 86% of total characters, and end up with 45,821 characters and 12,815 unique HLA, resulting in 945,519 total character-HLA pairs  Each collected character has 20 64 HLAs on average  See Figure FIGREF1 for an example character and their HLAs 
Methodology ::: Overall Task
Our task is the following, where $t$ denotes “target":
Given a target character $c_t$ with HLA set $H_t$, recover the language style of $c_t$ without any dialogue of $c_t$ provided 
For example, if Sheldon Cooper from The Big Bang Theory is $c_t$, then $H_t$ is the set of HLA on the left side of Figure FIGREF1 
We define the language style of a character as its diction, tone, and speech patterns  It is a character specific language model refined from a general language model  We must learn to recover the language style of $c_t$ without its dialogue as our objective is to imitate human-like qualities, and hence the model must understand the language styles of characters based on their traits  If we feed $c_t$'s dialogue during training, the model will likely not effectively learn to imitate language styles based on HLAs, but based on the correlation between text in the training and testing dialogues BIBREF26 
We define character space as the character representations within the HLA latent space (see Figure FIGREF4), and the set $C = \lbrace c_1,c_2,   ,c_n\rbrace $ as the set of all characters  We define Observation (OBS) as the input that is fed into any dialogue model  This can be a single or multiple lines of dialogue along with other information  The goal of the dialogue model is to find the best response to this OBS 
Methodology ::: ALOHA
We propose a three-component system called ALOHA to solve the task (see Figure FIGREF6)  The first component, Character Space Module (CSM), generates the character space and calculates confidence levels using singular value decomposition BIBREF27 between characters $c_j$ (for $j = 1$ to $n$ where $j \ne t$) and $c_t$ in the HLA-oriented neighborhood 
The second component, Character Community Module (CCM), ranks the similarity between our target character $c_t$ with any other character $c_j$ by the relative distance between them in the character space 
The third component, Language Style Recovery Module (LSRM), recovers the language style of $c_t$ without its dialogue by training the BERT bi-ranker model BIBREF28 to rank responses from similar characters  Our results demonstrate higher accuracy at retrieving the ground truth response from $c_t$  Our system is also able to pick responses which are correct both in context as well as character space 
Hence, the overall process for ALOHA works as follows  First, given a set of characters, determine the character space using the CSM  Next, given a specific target character, determine the positive community and negative set of associated characters using the CCM  Lastly, using the positive community and negative set determined above along with a dialogue dataset, recover the language style of the target 
Methodology ::: Character Space Module (CSM)
CSM learns how to rank characters  We can measure the interdependencies between the HLA variables BIBREF29 and rank the similarity between the TV show characters  We use implicit feedback instead of neighborhood models (e g  cosine similarity) because it can compute latent factors to transform both characters and HLAs into the same latent space, making them directly comparable 
We define a matrix $P$ that contains binary values, with $P_{u,i} = 1$ if character $u$ has HLA $i$ in our dataset, and $P_{u,i} = 0$ otherwise  We define a constant $\alpha $ that measures our confidence in observing various character-HLA pairs as positive  $\alpha $ controls how much the model penalizes the error if the ground truth is $P_{u,i} = 1$  If $P_{u,i} = 1$ and the model guesses incorrectly, we penalize by $\alpha $ times the loss  But if $P_{u,i} = 0$ and the model guesses a value greater than 0, we do not penalize as $\alpha $ has no impact  This is because $P_{u,i} = 0$ can either represent a true negative or be due to a lack of data, and hence is less reliable for penalization  See Equation DISPLAY_FORM8  We find that using $\alpha =20$ provides decent results 
We further define two dense vectors $X_u$ and $Y_i$  We call $X_u$ the “latent factors for character $u$", and $Y_i$ the “latent factors for HLA $i$"  The dot product of these two vectors produces a value ($X_u^TY_i$) that approximates $P_{u,i}$ (see Figure FIGREF9)  This is analogous to factoring the matrix $P$ into two separate matrices, where one contains the latent factors for characters, and the other contains the latent factors for HLAs  We find that $X_u$ and $Y_i$ being 36-dimensional produces decent results  To bring $X_u^TY_i$ as close as possible to $P_{u,i}$, we minimize the following loss function using the Conjugate Gradient Method BIBREF30:
The first term penalizes differences between the model's prediction ($X_u^TY_i$) and the actual value ($P_{u,i}$)  The second term is an L2 regularizer to reduce overfitting  We find $\lambda = 100$ provides decent results for 500 iterations (see Section SECREF26) 
Methodology ::: Character Community Module (CCM)
CCM aims to divide characters (other than $c_t$) into a positive community and a negative set  We define this positive community as characters that are densely connected internally to $c_t$ within the character space, and the negative set as the remaining characters  We can then sample dialogue from characters in the negative set to act as the distractors (essentially negative samples) during LSRM training 
As community finding is an ill-defined problem BIBREF31, we choose to treat CCM as a simple undirected, unweighted graph  We use the values learned in the CSM for $X_u$ and $Y_i$ for various values of $u$ and $i$, which approximate the matrix $P$  Similar to BIBREF29 hu2008collaborative, we can calculate the correlation between two rows (and hence two characters) 
We then employ a two-level connection representation by ranking all characters against each other in terms of their correlation with $c_t$  For the first level, the set $S^{FL}$ is the top 10% (4582) most highly correlated characters with $c_t$ out of the 45,820 total other characters that we have HLA data for  For the second level, for each character $s_i$ in $S^{FL}$, we determine the 30 most heavily correlated characters with $s_i$ as set $S^{SL}_i$  The positive set $S^{pos}$ are the characters which are present in at least 10 $S^{SL}_i$ sets  We call this value 10 the minimum frequency  All other characters in our dialogue dataset make up the negative set $S^{neg}$  These act as our positive community and negative set, respectively  See Algorithm 1 in Appendix A for details, and Figure FIGREF11 for an example 
Methodology ::: Language Style Recovery Module (LSRM)
LSRM creates a dialogue agent that aligns with observed characteristics of human characters by using the positive character community and negative set determined in the CCM, along with a dialogue dataset, to recover the language style of $c_t$ without its dialogue  We use the BERT bi-ranker model from the Facebook ParlAI framework BIBREF32, where the model has the ability to retrieve the best response out of 20 candidate responses  BIBREF12, BIBREF19, BIBREF0 choose 20 candidate responses, and for comparison purposes, we do the same 
Methodology ::: Language Style Recovery Module (LSRM) ::: BERT
BIBREF28 is first trained on massive amounts of unlabeled text data  It jointly conditions on text on both the left and right, which provides a deep bi-directional representation of sentence inference  BERT is proven to perform well on a wide range of tasks by simply fine-tuning on one additional layer  We are interested in its ability to predict the next sentence, called Next Sentence Prediction  We perform further fine-tuning on BERT for our target character language style retrieval task to produce our LSRM model by optimizing both the encoding layers and the additional layer  We use BERT to create vector representations for the OBS and for each candidate response  By passing the first output of BERT's 12 layers through an additional linear layer, these representations can be obtained as 768-dimensional sentence-level embeddings  It uses the dot product between these embeddings to score candidate responses and is trained using the ranking loss 
Methodology ::: Language Style Recovery Module (LSRM) ::: Candidate response selection
is similar to the procedure from previous work done on grounded dialogue agents BIBREF0, BIBREF19  Along with the ground truth response, we randomly sample 19 distractor responses from other characters from a uniform distribution of characters, and call this process uniform character sampling  Based on our observations, this random sampling provides multiple context correct responses  Hence, the BERT bi-ranker model is trained by learning to choose context correct responses, and the model learns to recover a domain-general language model that includes training on every character  This results in a Uniform Model that can select context correct responses, but not responses corresponding to a target character with specific HLAs 
We then fine-tune on the above model to produce our LSRM model with a modification: we randomly sample the 19 distractor responses from only the negative character set instead  We choose the responses that have similar grammatical structures and semantics to the ground truth response, and call this process negative character sampling  This guides the model away from the language style of these negative characters to improve performance at retrieving responses for target characters with specific HLAs  Our results demonstrate higher accuracy at retrieving the correct response from character $c_t$, which is the ground truth 
Experiment ::: Dialogue Dataset
To train the Uniform Model and LSRM, we collect dialogues from 327 major characters (a subset of the 45,821 characters we have HLA data for) in 38 TV shows from various existing sources of clean data on the internet, resulting in a total of 1,042,647 dialogue lines  We use a setup similar to the Persona-Chat dataset BIBREF0 and Cornell Movie-Dialogs Corpus BIBREF33, as our collected dialogues are also paired in terms of valid conversations  See Figure FIGREF1 for an example of these dialogue lines 
Experiment ::: HLA Observation Guidance (HLA-OG)
We define HLA Observation Guidance (HLA-OG) as explicitly passing a small subset of the most important HLAs of a given character as part of the OBS rather than just an initial line of dialogue  This is adapted from the process used in BIBREF0 zhang2018personalizing and BIBREF10 wolf2019transfertransfo which we call Persona Profiling  Specifically, we pass four HLAs that are randomly drawn from the top 40 most important HLAs of the character  We use HLA-OG during training of the LSRM and testing of all models  This is because the baselines (see Section SECREF31) already follow a similar process (Persona Profiling) for training  For the Uniform Model, we train using Next Sentence Prediction (see Section SECREF12)  For testing, HLA-OG is necessary as it provides information about which HLAs the models should attempt to imitate in their response selection  Just passing an initial line of dialogue replicates a typical dialogue response task without HLAs  See Table TABREF19  Further, we also test our LSRM by explicitly passing four HLAs of `none' along with the initial line of dialogue as the OBS (No HLA-OG in Table TABREF19) 
Experiment ::: Training Details ::: BERT bi-ranker
is trained by us on the Persona-Chat dataset for the ConvAI2 challenge  Similar to BIBREF0 zhang2018personalizing, we cap the length of the OBS at 360 tokens and the length of each candidate response at 72 tokens  We use a batch size of 64, learning rate of 5e-5, and perform warm-up updates for 100 iterations  The learning rate scheduler uses SGD optimizer with Nesterov's accelerated gradient descent BIBREF34 and is set to have a decay of 0 4 and to reduce on plateau 
Experiment ::: Training Details ::: Uniform Model
is produced by finetuning the BERT bi-ranker on the dialogue data discussed in Section SECREF15 using uniform character sampling  We use the same hyperparameters as the BERT bi-ranker along with half-precision operations (i e  float16 operations) to increase batch size as recommended BIBREF7 
Experiment ::: Training Details ::: LSRM
is produced by finetuning on the Uniform Model discussed above using negative character sampling  We use the same hyperparameters as the BERT bi-ranker along with half-precision operations (i e  float16 operations) to increase batch size as recommended 
Evaluation ::: CSM Evaluation
We begin by evaluating the ability of the CSM component of our system to correctly generate the character space  To do so, during training, 30% of the character-HLA pairs (which are either 0 or 1) are masked, and this is used as a validation set (see Figure FIGREF9)  For each character $c$, the model generates a list of the 12,815 unique HLAs ranked similarly to BIBREF29 hu2008collaborative for $c$  We look at the recall of our CSM model, which measures the percentage of total ground truth HLAs (over all characters $c$) present within the top N ranked HLAs for all $c$ by our model  That is:
where $HLA_{c}^{gt}$ are the ground truth HLAs for $c$, and $HLA_{c}^{tN}$ are the top N ranked HLAs by the model for $c$  We use $N = 100$, and our model achieves 25 08% recall 
To inspect the CSM performance, we use the T-distributed Stochastic Neighbor Embedding (t-SNE) BIBREF35 to reduce each high-dimensionality data point to two-dimensions via Kullback-Leibler Divergence BIBREF36  This allows us to map our character space into two-dimensions, where similar characters from our embedding space have higher probability of being mapped close by  We sampled characters from four different groups or regions  As seen in Figure FIGREF4, our learned character space effectively groups these characters, as similar characters are adjacent to one another in four regions 
Evaluation ::: Automatic Evaluation Setup ::: Five-Fold Cross Validation
is used for training and testing of the Uniform Model and LSRM  The folds are divided randomly by the TV shows in our dialogue data  We use the dialogue data for 80% of these shows as the four-folds for training, and the dialogue data for the remaining 20% as the fifth-fold for validation/testing  The dialogue data used is discussed in Section SECREF15  This ensures no matter how our data is distributed, each part of it is tested, allowing our evaluation to be more robust to different characters  See Appendix C for five-fold cross validation details and statistics 
Evaluation ::: Automatic Evaluation Setup ::: Five Evaluation Characters
are chosen, one from each of the five testing sets above  Each is a well-known character from a separate TV show, and acts as a target character $c_t$ for evaluation of every model  We choose Sheldon Cooper from The Big Bang Theory, Jean-Luc Picard from Star Trek, Monica Geller from Friends, Gil Grissom from CSI, and Marge Simpson from The Simpsons  We choose characters of significantly different identities and profiles (intelligent scientist, ship captain, outgoing friend, police leader, and responsible mother, respectively) from shows of a variety of genres to ensure that we can successfully recover the language styles of various types of characters  We choose well-known characters because humans require knowledge on the characters they are evaluating (see Section SECREF40) 
For each of these five evaluation characters, all the dialogue lines from the character act as the ground truth responses  The initial dialogue lines are the corresponding dialogue lines to which these ground truth responses are responding  For each initial dialogue line, we randomly sample 19 other candidate responses from the associated testing set using uniform character sampling  Note that this is for evaluation, and hence we use the same uniform character sampling method for all models including ALOHA  The use of negative character sampling is only in ALOHA's training 
Evaluation ::: Baselines
We compare against four dialogue system baselines: Kvmemnn, Feed Yourself, Poly-encoder, and a BERT bi-ranker baseline trained on the Persona-Chat dataset using the same training hyperparameters (including learning rate scheduler and length capping settings) described in Section SECREF20  For the first three models, we use the provided pretrained (on Persona-Chat) models  We evaluate all four on our five evaluation characters discussed in Section SECREF28 
Evaluation ::: Key Evaluation Metrics ::: Hits@n/N
is the accuracy of the correct ground truth response being within the top $n$ ranked candidate responses out of $N$ total candidates  We measure Hits@1/20, Hits@5/20, and Hits@10/20 
Evaluation ::: Key Evaluation Metrics ::: Mean Rank
is the average rank that a model assigns the ground truth response among the 20 total candidates 
Evaluation ::: Key Evaluation Metrics ::: Mean Reciprocal Rank (MRR)
BIBREF37 looks at the mean of the multiplicative inverses of the rank of each correct answer out of a sample of queries $Q$:
where $rank_i$ refers to the rank position of the correct response for the $i$-th query, and $|Q|$ refers to the total number of queries in $Q$ 
Evaluation ::: Key Evaluation Metrics ::: @!START@$F_1$@!END@-score
equals $2 * \frac{precision*recall}{precision+recall}$  For dialogue, precision is the fraction of words in the chosen response contained in the ground truth response, and recall is the fraction of words in the ground truth response contained in the chosen response 
Evaluation ::: Key Evaluation Metrics ::: BLEU
BIBREF38 generally indicates how close two pieces of text are in content and structure, with higher values indicating greater similarity  We report our final BLEU scores as the average scores of 1 to 4-grams 
Evaluation ::: Human Evaluation Setup
We conduct a human evaluation with 12 participants, 8 male and 4 female, who are affiliated project researchers aged 20-39 at the University of [ANON]  We choose the same five evaluation characters as in Section SECREF28  To control bias, each participant evaluates one or two characters  For each character, we randomly select 10 testing samples (each includes an initial line of dialogue along with 20 candidate responses, one of which is the ground truth) from the same testing data for the automatic evaluation discussed in Section SECREF28 
These ten samples make up a single questionnaire presented in full to each participant evaluating the corresponding character, and the participant is asked to select the single top response they think the character would most likely respond with for each of the ten initial dialogue lines  See Figure FIGREF41 for an example  We mask any character names within the candidate responses to prevent human participants from using names to identify which show the response is from 
Each candidate is prescreened to ensure they have sufficient knowledge of the character to be a participant  We ask three prescreening questions where the participant has to identify an image, relationship, and occupation of the character  All 12 of our participants passed the the prescreening 
Results and Analysis ::: Evaluation Results
Table TABREF44 shows average results of our automatic and human evaluations  Table TABREF45 shows average Hits@1/20 scores by evaluation character  See Appendix F for detailed evaluation results  ALOHA is the model with HLA-OG during training and testing, and ALOHA (No HLA-OG) is the model with HLA-OG during training but tested with the four HLAs in the OBS marked as `none' (see Section SECREF17)  See Appendix G for demo interactions between a human, BERT bi-ranker baseline, and ALOHA for all five evaluation characters 
Results and Analysis ::: Evaluation Challenges
The evaluation of our task (retrieving the language style of a specific character) is challenging and hence the five-fold cross validation is necessary for the following reasons:
The ability to choose a context correct response without attributes of specific characters may be hard to separate from our target metric, which is the ability to retrieve the correct response of a target character by its HLAs  However, from manual observation, we noticed that in the 20 chosen candidate responses, there are typically numerous context correct responses, but only one ground truth for the target character (for an example, see Figure FIGREF41)  Hence, a model that only chooses dialogue based on context is distinguishable from one that learns HLAs 
Retrieving responses for the target character depends on the other candidate responses  For example, dialogue retrieval performance for Grissom from CSI, which is a crime/police context, is higher than other evaluation characters (see Table TABREF45), potentially due to other candidate responses not falling within the same crime/police context 
Results and Analysis ::: Performance: ALOHA vs  Humans
As observed from Table TABREF44, ALOHA has a performance relatively close to humans  Human Hits@1/20 scores have a mean of 40 67% and a median over characters of 40%  The limited human evaluation sample size limits what can be inferred, but it indicates that the problem is solved to the extent that ALOHA is able perform relatively close to humans on average  Notice that even humans do not perform extremely well, demonstrating that this task of character based dialogue retrieval is more difficult than typical dialogue retrieval tasks BIBREF19, BIBREF12 
Looking more closely at each character from Table TABREF45, we can see that human evaluation scores are higher for Sheldon and Grissom  This may be due to these characters having more distinct personalities, making them more memorable 
We also look at Pearson correlation values of the Hits@1/20 scores across the five evaluation characters  For human versus Uniform Model, this is -0 4694, demonstrating that the Uniform Model, without knowledge of HLAs, fails to imitate human impressions  For human versus ALOHA, this is 0 4250, demonstrating that our system is able to retrieve character responses somewhat similarly to human impressions  Lastly, for human versus the difference in scores between ALOHA and Uniform Model, this is 0 7815  The difference between ALOHA and the Uniform Model, which is based on the additional knowledge of the HLAs, is hence shown to improve upon the Uniform Model similarly to human impressions  This demonstrates that HLAs are indeed an accurate method of modeling human impressions of character attributes, and also demonstrates that our system, ALOHA, is able to effectively use these HLAs to improve upon dialogue retrieval performance 
Results and Analysis ::: Performance: ALOHA vs  Baselines
ALOHA, combined with the HLAs and dialogue dataset, achieves a significant improvement on the target character language style retrieval task compared to the baseline open-domain chatbot models  As observed from Table TABREF44, ALOHA achieves a significant boost in Hits@n/N accuracy and other metrics for retrieving the correct response of five diverse characters with different identities (see Section SECREF28) 
Results and Analysis ::: Performance: ALOHA vs  Uniform Model
We observe a noticeable improvement in performance between ALOHA and the Uniform Model in recovering the language styles of specific characters that is consistent across all five folds (see Tables TABREF44 and TABREF45), indicating that lack of knowledge of HLAs limits the ability of the model to successfully recover the language style of specific characters  We claim that, to the best of our knowledge, we have made the first step in using HLA-based character dialogue clustering to improve upon personality learning for chatbots 
ALOHA demonstrates an accuracy boost for all five evaluation characters, showing that the system is robust and stable and has the ability to recover the dialogue styles of fictional characters regardless of the character's profile and identity, genre of the show, and context of the dialogue 
Results and Analysis ::: Performance: HLA-OG
As observed from Table TABREF44, ALOHA performs slightly better overall compared to ALOHA (No HLA-OG)  Table TABREF45 shows that this slight performance increase is consistent across four of the five evaluation characters  In the case of Sheldon, the HLA-OG model performs a bit worse  This is possibly due to the large number of Sheldon's HLAs (217) compared to the other four evaluation characters (average of 93 75), along with the limited amount of HLAs we are using for guidance due to the models' limited memory  In general, HLA Observation Guidance during testing appears to improve upon the performance of ALOHA, but this improvement is minimal 
Conclusion and Future Work
We proposed Human Level Attributes (HLAs) as a novel approach to model human-like attributes of characters, and collected a large volume of dialogue data for various characters with complete and robust profiles  We also proposed and evaluated a system, ALOHA, that uses HLAs to recommend tailored responses traceable to specific characters, and demonstrated its outperformance of the baselines and ability to effectively recover language styles of various characters, showing promise for learning character or personality styles  ALOHA was also shown to be stable regardless of the character's identity, genre of show, and context of dialogue 
Potential directions for future work include training ALOHA with a multi-turn response approach BIBREF0 that tracks dialogue over multiple responses, as we could not acquire multi-turn dialogue data for TV shows  Another potential is the modeling of the dialog counterpart (e g  the dialogue of other characters speaking to the target character)  Further, performing semantic text exchange on the chosen response with a model such as SMERTI BIBREF39 may improve the ability of ALOHA to converse with humans  This is because the response may be context and HLA correct, but incorrect semantically (e g  the response may say the weather is sunny when it is actually rainy)  HLA-aligned generative models is another area of exploration  Typically, generative models produce text that is less fluent, but further work in this area may lead to better results  Lastly, a more diverse and larger participant pool is required due to the limited size of our human evaluation 

ground_truth: Metric difference between Aloha and best baseline score:
Hits@1/20: +0.061 (0.3642 vs 0.3032)
MRR: +0.0572(0.5114 vs 0.4542)
F1: -0.0484 (0.3901 vs 0.4385)
BLEU: +0.0474 (0.2867 vs 0.2393)

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. What baseline model is used?
Context 2: Introduction
Wikipedia is the largest source of open and collaboratively curated knowledge in the world  Introduced in 2001, it has evolved into a reference work with around 5m pages for the English Wikipedia alone  In addition, entities and event pages are updated quickly via collaborative editing and all edits are encouraged to include source citations, creating a knowledge base which aims at being both timely as well as authoritative  As a result, it has become the preferred source of information consumption about entities and events  Moreso, this knowledge is harvested and utilized in building knowledge bases like YAGO BIBREF0 and DBpedia BIBREF1 , and used in applications like text categorization BIBREF2 , entity disambiguation BIBREF3 , entity ranking BIBREF4 and distant supervision BIBREF5 , BIBREF6  
However, not all Wikipedia pages referring to entities (entity pages) are comprehensive: relevant information can either be missing or added with a delay  Consider the city of New Orleans and the state of Odisha which were severely affected by cyclones Hurricane Katrina and Odisha Cyclone, respectively  While Katrina finds extensive mention in the entity page for New Orleans, Odisha Cyclone which has 5 times more human casualties (cf  Figure FIGREF2 ) is not mentioned in the page for Odisha  Arguably Katrina and New Orleans are more popular entities, but Odisha Cyclone was also reported extensively in national and international news outlets  This highlights the lack of important facts in trunk and long-tail entity pages, even in the presence of relevant sources  In addition, previous studies have shown that there is an inherent delay or lag when facts are added to entity pages BIBREF7  
To remedy these problems, it is important to identify information sources that contain novel and salient facts to a given entity page  However, not all information sources are equal  The online presence of major news outlets is an authoritative source due to active editorial control and their articles are also a timely container of facts  In addition, their use is in line with current Wikipedia editing practice, as is shown in BIBREF7 that almost 20% of current citations in all entity pages are news articles  We therefore propose news suggestion as a novel task that enhances entity pages and reduces delay while keeping its pages authoritative 
Existing efforts to populate Wikipedia BIBREF8 start from an entity page and then generate candidate documents about this entity using an external search engine (and then post-process them)  However, such an approach lacks in (a) reproducibility since rankings vary with time with obvious bias to recent news (b) maintainability since document acquisition for each entity has to be periodically performed  To this effect, our news suggestion considers a news article as input, and determines if it is valuable for Wikipedia  Specifically, given an input news article INLINEFORM0 and a state of Wikipedia, the news suggestion problem identifies the entities mentioned in INLINEFORM1 whose entity pages can improve upon suggesting INLINEFORM2   Most of the works on knowledge base acceleration BIBREF9 , BIBREF10 , BIBREF11 , or Wikipedia page generation BIBREF8 rely on high quality input sources which are then utilized to extract textual facts for Wikipedia page population  In this work, we do not suggest snippets or paraphrases but rather entire articles which have a high potential importance for entity pages  These suggested news articles could be consequently used for extraction, summarization or population either manually or automatically – all of which rely on high quality and relevant input sources 
We identify four properties of good news recommendations: salience, relative authority, novelty and placement  First, we need to identify the most salient entities in a news article  This is done to avoid pollution of entity pages with only marginally related news  Second, we need to determine whether the news is important to the entity as only the most relevant news should be added to a precise reference work  To do this, we compute the relative authority of all entities in the news article: we call an entity more authoritative than another if it is more popular or noteworthy in the real world  Entities with very high authority have many news items associated with them and only the most relevant of these should be included in Wikipedia whereas for entities of lower authority the threshold for inclusion of a news article will be lower  Third, a good recommendation should be able to identify novel news by minimizing redundancy coming from multiple news articles  Finally, addition of facts is facilitated if the recommendations are fine-grained, i e , recommendations are made on the section level rather than the page level (placement) 
Approach and Contributions  We propose a two-stage news suggestion approach to entity pages  In the first stage, we determine whether a news article should be suggested for an entity, based on the entity's salience in the news article, its relative authority and the novelty of the article to the entity page  The second stage takes into account the class of the entity for which the news is suggested and constructs section templates from entities of the same class  The generation of such templates has the advantage of suggesting and expanding entity pages that do not have a complete section structure in Wikipedia, explicitly addressing long-tail and trunk entities  Afterwards, based on the constructed template our method determines the best fit for the news article with one of the sections 
We evaluate the proposed approach on a news corpus consisting of 351,982 articles crawled from the news external references in Wikipedia from 73,734 entity pages  Given the Wikipedia snapshot at a given year (in our case [2009-2014]), we suggest news articles that might be cited in the coming years  The existing news references in the entity pages along with their reference date act as our ground-truth to evaluate our approach  In summary, we make the following contributions 
Related Work
As we suggest a new problem there is no current work addressing exactly the same task  However, our task has similarities to Wikipedia page generation and knowledge base acceleration  In addition, we take inspiration from Natural Language Processing (NLP) methods for salience detection 
Wikipedia Page Generation is the problem of populating Wikipedia pages with content coming from external sources  Sauper and Barzilay BIBREF8 propose an approach for automatically generating whole entity pages for specific entity classes  The approach is trained on already-populated entity pages of a given class (e g  `Diseases') by learning templates about the entity page structure (e g  diseases have a treatment section)  For a new entity page, first, they extract documents via Web search using the entity title and the section title as a query, for example `Lung Cancer'+`Treatment'  As already discussed in the introduction, this has problems with reproducibility and maintainability  However, their main focus is on identifying the best paragraphs extracted from the collected documents  They rank the paragraphs via an optimized supervised perceptron model for finding the most representative paragraph that is the least similar to paragraphs in other sections  This paragraph is then included in the newly generated entity page  Taneva and Weikum BIBREF12 propose an approach that constructs short summaries for the long tail  The summaries are called `gems' and the size of a `gem' can be user defined  They focus on generating summaries that are novel and diverse  However, they do not consider any structure of entities, which is present in Wikipedia 
In contrast to BIBREF8 and BIBREF12 , we actually focus on suggesting entire documents to Wikipedia entity pages  These are authoritative documents (news), which are highly relevant for the entity, novel for the entity and in which the entity is salient  Whereas relevance in Sauper and Barzilay is implicitly computed by web page ranking we solve that problem by looking at relative authority and salience of an entity, using the news article and entity page only  As Sauper and Barzilay concentrate on empty entity pages, the problem of novelty of their content is not an issue in their work whereas it is in our case which focuses more on updating entities  Updating entities will be more and more important the bigger an existing reference work is  Both the approaches in BIBREF8 and BIBREF12 (finding paragraphs and summarization) could then be used to process the documents we suggest further  Our concentration on news is also novel 
Knowledge Base Acceleration  In this task, given specific information extraction templates, a given corpus is analyzed in order to find worthwhile mentions of an entity or snippets that match the templates  Balog BIBREF9 , BIBREF10 recommend news citations for an entity  Prior to that, the news articles are classified for their appropriateness for an entity, where as features for the classification task they use entity, document, entity-document and temporal features  The best performing features are those that measure similarity between an entity and the news document  West et al  BIBREF13 consider the problem of knowledge base completion, through question answering and complete missing facts in Freebase based on templates, i e  Frank_Zappa bornIn Baltymore, Maryland 
In contrast, we do not extract facts for pre-defined templates but rather suggest news articles based on their relevance to an entity  In cases of long-tail entities, we can suggest to add a novel section through our abstraction and generation of section templates at entity class level 
Entity Salience  Determining which entities are prominent or salient in a given text has a long history in NLP, sparked by the linguistic theory of Centering BIBREF14   Salience has been used in pronoun and co-reference resolution BIBREF15 , or to predict which entities will be included in an abstract of an article BIBREF11   Frequent features to measure salience include the frequency of an entity in a document, positioning of an entity, grammatical function or internal entity structure (POS tags, head nouns etc )  These approaches are not currently aimed at knowledge base generation or Wikipedia coverage extension but we postulate that an entity's salience in a news article is a prerequisite to the news article being relevant enough to be included in an entity page  We therefore use the salience features in BIBREF11 as part of our model  However, these features are document-internal — we will show that they are not sufficient to predict news inclusion into an entity page and add features of entity authority, news authority and novelty that measure the relations between several entities, between entity and news article as well as between several competing news articles 
Terminology and Problem Definition
We are interested in named entities mentioned in documents  An entity INLINEFORM0 can be identified by a canonical name, and can be mentioned differently in text via different surface forms  We canonicalize these mentions to entity pages in Wikipedia, a method typically known as entity linking  We denote the set of canonicalized entities extracted and linked from a news article INLINEFORM1 as INLINEFORM2   For example, in Figure FIGREF7 , entities are canonicalized into Wikipedia entity pages (e g  Odisha is canonicalized to the corresponding article)  For a collection of news articles INLINEFORM3 , we further denote the resulting set of entities by INLINEFORM4  
Information in an entity page is organized into sections and evolves with time as more content is added  We refer to the state of Wikipedia at a time INLINEFORM0 as INLINEFORM1 and the set of sections for an entity page INLINEFORM2 as its entity profile INLINEFORM3   Unlike news articles, text in Wikipedia could be explicitly linked to entity pages through anchors  The set of entities explicitly referred in text from section INLINEFORM4 is defined as INLINEFORM5   Furthermore, Wikipedia induces a category structure over its entities, which is exploited by knowledge bases like YAGO (e g  Barack_Obama isA Person)  Consequently, each entity page belongs to one or more entity categories or classes INLINEFORM6   Now we can define our news suggestion problem below:
Definition 1 (News Suggestion Problem) Given a set of news articles INLINEFORM0 and set of Wikipedia entity pages INLINEFORM1 (from INLINEFORM2 ) we intend to suggest a news article INLINEFORM3 published at time INLINEFORM4 to entity page INLINEFORM5 and additionally to the most relevant section for the entity page INLINEFORM6  
Approach Overview
We approach the news suggestion problem by decomposing it into two tasks:
AEP: Article–Entity placement
ASP: Article–Section placement
In this first step, for a given entity-news pair INLINEFORM0 , we determine whether the given news article INLINEFORM1 should be suggested (we will refer to this as `relevant') to entity INLINEFORM2   To generate such INLINEFORM3 pairs, we perform the entity linking process, INLINEFORM4 , for INLINEFORM5  
The article–entity placement task (described in detail in Section SECREF16 ) for a pair INLINEFORM0 outputs a binary label (either `non-relevant' or `relevant') and is formalized in Equation EQREF14   DISPLAYFORM0
In the second step, we take into account all `relevant' pairs INLINEFORM0 and find the correct section for article INLINEFORM1 in entity INLINEFORM2 , respectively its profile INLINEFORM3 (see Section SECREF30 )  The article–section placement task, determines the correct section for the triple INLINEFORM4 , and is formalized in Equation EQREF15   DISPLAYFORM0
In the subsequent sections we describe in details how we approach the two tasks for suggesting news articles to entity pages 
News Article Suggestion
In this section, we provide an overview of the news suggestion approach to Wikipedia entity pages (see Figure FIGREF7 )  The approach is split into two tasks: (i) article-entity (AEP) and (ii) article-section (ASP) placement  For a Wikipedia snapshot INLINEFORM0 and a news corpus INLINEFORM1 , we first determine which news articles should be suggested to an entity INLINEFORM2   We will denote our approach for AEP by INLINEFORM3   Finally, we determine the most appropriate section for the ASP task and we denote our approach with INLINEFORM4  
In the following, we describe the process of learning the functions INLINEFORM0 and INLINEFORM1   We introduce features for the learning process, which encode information regarding the entity salience, relative authority and novelty in the case of AEP task  For the ASP task, we measure the overall fit of an article to the entity sections, with the entity being an input from AEP task  Additionally, considering that the entity profiles INLINEFORM2 are incomplete, in the case of a missing section we suggest and expand the entity profiles based on section templates generated from entities of the same class INLINEFORM3 (see Section UID34 ) 
Article–Entity Placement
In this step we learn the function INLINEFORM0 to correctly determine whether INLINEFORM1 should be suggested for INLINEFORM2 , basically a binary classification model (0=`non-relevant' and 1=`relevant')  Note that we are mainly interested in finding the relevant pairs in this task  For every news article, the number of disambiguated entities is around 30 (but INLINEFORM3 is suggested for only two of them on average)  Therefore, the distribution of `non-relevant' and `relevant' pairs is skewed towards the earlier, and by simply choosing the `non-relevant' label we can achieve a high accuracy for INLINEFORM4   Finding the relevant pairs is therefore a considerable challenge 
An article INLINEFORM0 is suggested to INLINEFORM1 by our function INLINEFORM2 if it fulfills the following properties  The entity INLINEFORM3 is salient in INLINEFORM4 (a central concept), therefore ensuring that INLINEFORM5 is about INLINEFORM6 and that INLINEFORM7 is important for INLINEFORM8   Next, given the fact there might be many articles in which INLINEFORM9 is salient, we also look at the reverse property, namely whether INLINEFORM10 is important for INLINEFORM11   We do this by comparing the authority of INLINEFORM12 (which is a measure of popularity of an entity, such as its frequency of mention in a whole corpus) with the authority of its co-occurring entities in INLINEFORM13 , leading to a feature we call relative authority  The intuition is that for an entity that has overall lower authority than its co-occurring entities, a news article is more easily of importance  Finally, if the article we are about to suggest is already covered in the entity profile INLINEFORM14 , we do not wish to suggest redundant information, hence the novelty  Therefore, the learning objective of INLINEFORM15 should fulfill the following properties  Table TABREF21 shows a summary of the computed features for INLINEFORM16  
Salience: entity INLINEFORM0 should be a salient entity in news article INLINEFORM1
Relative Authority: the set of entities INLINEFORM0 with which INLINEFORM1 co-occurs should have higher authority than INLINEFORM2 , making INLINEFORM3 important for INLINEFORM4
Novelty: news article INLINEFORM0 should provide novel information for entity INLINEFORM1 taking into account its profile INLINEFORM2
Baseline Features  As discussed in Section SECREF2 , a variety of features that measure salience of an entity in text are available from the NLP community  We reimplemented the ones in Dunietz and Gillick BIBREF11   This includes a variety of features, e g  positional features, occurrence frequency and the internal POS structure of the entity and the sentence it occurs in  Table 2 in BIBREF11 gives details 
Relative Entity Frequency  Although frequency of mention and positional features play some role in baseline features, their interaction is not modeled by a single feature nor do the positional features encode more than sentence position  We therefore suggest a novel feature called relative entity frequency, INLINEFORM0 , that has three properties : (i) It rewards entities for occurring throughout the text instead of only in some parts of the text, measured by the number of paragraphs it occurs in (ii) it rewards entities that occur more frequently in the opening paragraphs of an article as we model INLINEFORM1 as an exponential decay function  The decay corresponds to the positional index of the news paragraph  This is inspired by the news-specific discourse structure that tends to give short summaries of the most important facts and entities in the opening paragraphs  (iii) it compares entity frequency to the frequency of its co-occurring mentions as the weight of an entity appearing in a specific paragraph, normalized by the sum of the frequencies of other entities in INLINEFORM2   DISPLAYFORM0
where, INLINEFORM0 represents a news paragraph from INLINEFORM1 , and with INLINEFORM2 we indicate the set of all paragraphs in INLINEFORM3   The frequency of INLINEFORM4 in a paragraph INLINEFORM5 is denoted by INLINEFORM6   With INLINEFORM7 and INLINEFORM8 we indicate the number of paragraphs in which entity INLINEFORM9 occurs, and the total number of paragraphs, respectively 
Relative Authority  In this case, we consider the comparative relevance of the news article to the different entities occurring in it  As an example, let us consider the meeting of the Sudanese bishop Elias Taban with Hillary Clinton  Both entities are salient for the meeting  However, in Taban's Wikipedia page, this meeting is discussed prominently with a corresponding news reference, whereas in Hillary Clinton's Wikipedia page it is not reported at all  We believe this is not just an omission in Clinton's page but mirrors the fact that for the lesser known Taban the meeting is big news whereas for the more famous Clinton these kind of meetings are a regular occurrence, not all of which can be reported in what is supposed to be a selection of the most important events for her  Therefore, if two entities co-occur, the news is more relevant for the entity with the lower a priori authority 
The a priori authority of an entity (denoted by INLINEFORM0 ) can be measured in several ways  We opt for two approaches: (i) probability of entity INLINEFORM1 occurring in the corpus INLINEFORM2 , and (ii) authority assessed through centrality measures like PageRank BIBREF16   For the second case we construct the graph INLINEFORM3 consisting of entities in INLINEFORM4 and news articles in INLINEFORM5 as vertices  The edges are established between INLINEFORM6 and entities in INLINEFORM7 , that is INLINEFORM8 , and the out-links from INLINEFORM9 , that is INLINEFORM10 (arrows present the edge direction) 
Starting from a priori authority, we proceed to relative authority by comparing the a priori authority of co-occurring entities in INLINEFORM0   We define the relative authority of INLINEFORM1 as the proportion of co-occurring entities INLINEFORM2 that have a higher a priori authority than INLINEFORM3 (see Equation EQREF28   DISPLAYFORM0
As we might run the danger of not suggesting any news articles for entities with very high a priori authority (such as Clinton) due to the strict inequality constraint, we can relax the constraint such that the authority of co-occurring entities is above a certain threshold 
News Domain Authority  The news domain authority addresses two main aspects  Firstly, if bundled together with the relative authority feature, we can ensure that dependent on the entity authority, we suggest news from authoritative sources, hence ensuring the quality of suggested articles  The second aspect is in a news streaming scenario where multiple news domains report the same event — ideally only articles coming from authoritative sources would fulfill the conditions for the news suggestion task 
The news domain authority is computed based on the number of news references in Wikipedia coming from a particular news domain INLINEFORM0   This represents a simple prior that a news article INLINEFORM1 is from domain INLINEFORM2 in corpus INLINEFORM3   We extract the domains by taking the base URLs from the news article URLs 
An important feature when suggesting an article INLINEFORM0 to an entity INLINEFORM1 is the novelty of INLINEFORM2 w r t the already existing entity profile INLINEFORM3   Studies BIBREF17 have shown that on comparable collections to ours (TREC GOV2) the number of duplicates can go up to INLINEFORM4   This figure is likely higher for major events concerning highly authoritative entities on which all news media will report 
Given an entity INLINEFORM0 and the already added news references INLINEFORM1 up to year INLINEFORM2 , the novelty of INLINEFORM3 at year INLINEFORM4 is measured by the KL divergence between the language model of INLINEFORM5 and articles in INLINEFORM6   We combine this measure with the entity overlap of INLINEFORM7 and INLINEFORM8   The novelty value of INLINEFORM9 is given by the minimal divergence value  Low scores indicate low novelty for the entity profile INLINEFORM10  
N(n|e) = n'Nt-1{DKL((n') || (n)) + DKL((N) || (n)) 
DKL((n') || (n))  (1-) jaccard((n'),(n))} where INLINEFORM0 is the KL divergence of the language models ( INLINEFORM1 and INLINEFORM2 ), whereas INLINEFORM3 is the mixing weight ( INLINEFORM4 ) between the language models INLINEFORM5 and the entity overlap in INLINEFORM6 and INLINEFORM7  
Here we introduce the evaluation setup and analyze the results for the article–entity (AEP) placement task  We only report the evaluation metrics for the `relevant' news-entity pairs  A detailed explanation on why we focus on the `relevant' pairs is provided in Section SECREF16  
Baselines  We consider the following baselines for this task 
B1  The first baseline uses only the salience-based features by Dunietz and Gillick BIBREF11  
B2  The second baseline assigns the value relevant to a pair INLINEFORM0 , if and only if INLINEFORM1 appears in the title of INLINEFORM2  
Learning Models  We use Random Forests (RF) BIBREF23   We learn the RF on all computed features in Table TABREF21   The optimization on RF is done by splitting the feature space into multiple trees that are considered as ensemble classifiers  Consequently, for each classifier it computes the margin function as a measure of the average count of predicting the correct class in contrast to any other class  The higher the margin score the more robust the model 
Metrics  We compute precision P, recall R and F1 score for the relevant class  For example, precision is the number of news-entity pairs we correctly labeled as relevant compared to our ground truth divided by the number of all news-entity pairs we labeled as relevant 
The following results measure the effectiveness of our approach in three main aspects: (i) overall performance of INLINEFORM0 and comparison to baselines, (ii) robustness across the years, and (iii) optimal model for the AEP placement task 
Performance  Figure FIGREF55 shows the results for the years 2009 and 2013, where we optimized the learning objective with instances from year INLINEFORM0 and evaluate on the years INLINEFORM1 (see Section SECREF46 )  The results show the precision–recall curve  The red curve shows baseline B1 BIBREF11 , and the blue one shows the performance of INLINEFORM2   The curve shows for varying confidence scores (high to low) the precision on labeling the pair INLINEFORM3 as `relevant'  In addition, at each confidence score we can compute the corresponding recall for the `relevant' label  For high confidence scores on labeling the news-entity pairs, the baseline B1 achieves on average a precision score of P=0 50, while INLINEFORM4 has P=0 93  We note that with the drop in the confidence score the corresponding precision and recall values drop too, and the overall F1 score for B1 is around F1=0 2, in contrast we achieve an average score of F1=0 67 
It is evident from Figure FIGREF55 that for the years 2009 and 2013, INLINEFORM0 significantly outperforms the baseline B1  We measure the significance through the t-test statistic and get a p-value of INLINEFORM1   The improvement we achieve over B1 in absolute numbers, INLINEFORM2 P=+0 5 in terms of precision for the years between 2009 and 2014, and a similar improvement in terms of F1 score  The improvement for recall is INLINEFORM3 R=+0 4  The relative improvement over B1 for P and F1 is almost 1 8 times better, while for recall we are 3 5 times better  In Table TABREF58 we show the overall scores for the evaluation metrics for B1 and INLINEFORM4   Finally, for B2 we achieve much poorer performance, with average scores of P=0 21, R=0 20 and F1=0 21 
Robustness  In Table TABREF58 , we show the overall performance for the years between 2009 and 2013  An interesting observation we make is that we have a very robust performance and the results are stable across the years  If we consider the experimental setup, where for year INLINEFORM0 we optimize the learning objective with only 74k training instances and evaluate on the rest of the instances, it achieves a very good performance  We predict with F1=0 68 the remaining 469k instances for the years INLINEFORM1  
The results are particularly promising considering the fact that the distribution between our two classes is highly skewed  On average the number of `relevant' pairs account for only around INLINEFORM0 of all pairs  A good indicator to support such a statement is the kappa (denoted by INLINEFORM1 ) statistic  INLINEFORM2 measures agreement between the algorithm and the gold standard on both labels while correcting for chance agreement (often expected due to extreme distributions)  The INLINEFORM3 scores for B1 across the years is on average INLINEFORM4 , while for INLINEFORM5 we achieve a score of INLINEFORM6 (the maximum score for INLINEFORM7 is 1) 
In Figure FIGREF60 we show the impact of the individual feature groups that contribute to the superior performance in comparison to the baselines  Relative entity frequency from the salience feature, models the entity salience as an exponentially decaying function based on the positional index of the paragraph where the entity appears  The performance of INLINEFORM0 with relative entity frequency from the salience feature group is close to that of all the features combined  The authority and novelty features account to a further improvement in terms of precision, by adding roughly a 7%-10% increase  However, if both feature groups are considered separately, they significantly outperform the baseline B1 
Article–Section Placement
We model the ASP placement task as a successor of the AEP task  For all the `relevant' news entity pairs, the task is to determine the correct entity section  Each section in a Wikipedia entity page represents a different topic  For example, Barack Obama has the sections `Early Life', `Presidency', `Family and Personal Life' etc  However, many entity pages have an incomplete section structure  Incomplete or missing sections are due to two Wikipedia properties  First, long-tail entities miss information and sections due to their lack of popularity  Second, for all entities whether popular or not, certain sections might occur for the first time due to real world developments  As an example, the entity Germanwings did not have an `Accidents' section before this year's disaster, which was the first in the history of the airline 
Even if sections are missing for certain entities, similar sections usually occur in other entities of the same class (e g  other airlines had disasters and therefore their pages have an accidents section)  We exploit such homogeneity of section structure and construct templates that we use to expand entity profiles  The learning objective for INLINEFORM0 takes into account the following properties:
Section-templates: account for incomplete section structure for an entity profile INLINEFORM0 by constructing section templates INLINEFORM1 from an entity class INLINEFORM2
Overall fit: measures the overall fit of a news article to sections in the section templates INLINEFORM0
Given the fact that entity profiles are often incomplete, we construct section templates for every entity class  We group entities based on their class INLINEFORM0 and construct section templates INLINEFORM1   For different entity classes, e g  Person and Location, the section structure and the information represented in those section varies heavily  Therefore, the section templates are with respect to the individual classes in our experimental setup (see Figure FIGREF42 )  DISPLAYFORM0
Generating section templates has two main advantages  Firstly, by considering class-based profiles, we can overcome the problem of incomplete individual entity profiles and thereby are able to suggest news articles to sections that do not yet exist in a specific entity INLINEFORM0   The second advantage is that we are able to canonicalize the sections, i e  `Early Life' and `Early Life and Childhood' would be treated similarly 
To generate the section template INLINEFORM0 , we extract all sections from entities of a given type INLINEFORM1 at year INLINEFORM2   Next, we cluster the entity sections, based on an extended version of k–means clustering BIBREF18 , namely x–means clustering introduced in Pelleg et al  which estimates the number of clusters efficiently BIBREF19   As a similarity metric we use the cosine similarity computed based on the tf–idf models of the sections  Using the x–means algorithm we overcome the requirement to provide the number of clusters k beforehand  x–means extends the k–means algorithm, such that a user only specifies a range [ INLINEFORM3 , INLINEFORM4 ] that the number of clusters may reasonably lie in 
The learning objective of INLINEFORM0 is to determine the overall fit of a news article INLINEFORM1 to one of the sections in a given section template INLINEFORM2   The template is pre-determined by the class of the entity for which the news is suggested as relevant by INLINEFORM3   In all cases, we measure how well INLINEFORM4 fits each of the sections INLINEFORM5 as well as the specific entity section INLINEFORM6   The section profiles in INLINEFORM7 represent the aggregated entity profiles from all entities of class INLINEFORM8 at year INLINEFORM9  
To learn INLINEFORM0 we rely on a variety of features that consider several similarity aspects as shown in Table TABREF31   For the sake of simplicity we do not make the distinction in Table TABREF31 between the individual entity section and class-based section similarities, INLINEFORM1 and INLINEFORM2 , respectively  Bear in mind that an entity section INLINEFORM3 might be present at year INLINEFORM4 but not at year INLINEFORM5 (see for more details the discussion on entity profile expansion in Section UID69 ) 
Topic  We use topic similarities to ensure (i) that the content of INLINEFORM0 fits topic-wise with a specific section text and (ii) that it has a similar topic to previously referred news articles in that section  In a pre-processing stage we compute the topic models for the news articles, entity sections INLINEFORM1 and the aggregated class-based sections in INLINEFORM2   The topic models are computed using LDA BIBREF20   We only computed a single topic per article/section as we are only interested in topic term overlaps between article and sections  We distinguish two main features: the first feature measures the overlap of topic terms between INLINEFORM3 and the entity section INLINEFORM4 and INLINEFORM5 , and the second feature measures the overlap of the topic model of INLINEFORM6 against referred news articles in INLINEFORM7 at time INLINEFORM8  
Syntactic  These features represent a mechanism for conveying the importance of a specific text snippet, solely based on the frequency of specific POS tags (i e  NNP, CD etc ), as commonly used in text summarization tasks  Following the same intuition as in BIBREF8 , we weigh the importance of articles by the count of specific POS tags  We expect that for different sections, the importance of POS tags will vary  We measure the similarity of POS tags in a news article against the section text  Additionally, we consider bi-gram and tri-gram POS tag overlap  This exploits similarity in syntactical patterns between the news and section text 
Lexical  As lexical features, we measure the similarity of INLINEFORM0 against the entity section text INLINEFORM1 and the aggregate section text INLINEFORM2   Further, we distinguish between the overall similarity of INLINEFORM3 and that of the different news paragraphs ( INLINEFORM4 which denotes the paragraphs of INLINEFORM5 up to the 5th paragraph)  A higher similarity on the first paragraphs represents a more confident indicator that INLINEFORM6 should be suggested to a specific section INLINEFORM7   We measure the similarity based on two metrics: (i) the KL-divergence between the computed language models and (ii) cosine similarity of the corresponding paragraph text INLINEFORM8 and section text 
Entity-based  Another feature set we consider is the overlap of named entities and their corresponding entity classes  For different entity sections, we expect to find a particular set of entity classes that will correlate with the section, e g  `Early Life' contains mostly entities related to family, school, universities etc 
Frequency  Finally, we gather statistics about the number of entities, paragraphs, news article length, top– INLINEFORM0 entities and entity classes, and the frequency of different POS tags  Here we try to capture patterns of articles that are usually cited in specific sections 
Evaluation Plan
In this section we outline the evaluation plan to verify the effectiveness of our learning approaches  To evaluate the news suggestion problem we are faced with two challenges 
What comprises the ground truth for such a task ?
How do we construct training and test splits given that entity pages consists of text added at different points in time ?
Consider the ground truth challenge  Evaluating if an arbitrary news article should be included in Wikipedia is both subjective and difficult for a human if she is not an expert  An invasive approach, which was proposed by Barzilay and Sauper BIBREF8 , adds content directly to Wikipedia and expects the editors or other users to redact irrelevant content over a period of time  The limitations of such an evaluation technique is that content added to long-tail entities might not be evaluated by informed users or editors in the experiment time frame  It is hard to estimate how much time the added content should be left on the entity page  A more non-invasive approach could involve crowdsourcing of entity and news article pairs in an IR style relevance assessment setup  The problem of such an approach is again finding knowledgeable users or experts for long-tail entities  Thus the notion of relevance of a news recommendation is challenging to evaluate in a crowd setup 
We take a slightly different approach by making an assumption that the news articles already present in Wikipedia entity pages are relevant  To this extent, we extract a dataset comprising of all news articles referenced in entity pages (details in Section SECREF40 )  At the expense of not evaluating the space comprising of news articles absent in Wikipedia, we succeed in (i) avoiding restrictive assumptions about the quality of human judgments, (ii) being invasive and polluting Wikipedia, and (iii) deriving a reusable test bed for quicker experimentation 
The second challenge of construction of training and test set separation is slightly easier and is addressed in Section SECREF46  
Datasets
The datasets we use for our experimental evaluation are directly extracted from the Wikipedia entity pages and their revision history  The generated data represents one of the contributions of our paper  The datasets are the following:
Entity Classes  We focus on a manually predetermined set of entity classes for which we expect to have news coverage  The number of analyzed entity classes is 27, including INLINEFORM0 entities with at least one news reference  The entity classes were selected from the DBpedia class ontology  Figure FIGREF42 shows the number of entities per class for the years (2009-2014) 
News Articles  We extract all news references from the collected Wikipedia entity pages  The extracted news references are associated with the sections in which they appear  In total there were INLINEFORM0 news references, and after crawling we end up with INLINEFORM1 successfully crawled news articles  The details of the news article distribution, and the number of entities and sections from which they are referred are shown in Table TABREF44  
Article-Entity Ground-truth  The dataset comprises of the news and entity pairs INLINEFORM0   News-entity pairs are relevant if the news article is referenced in the entity page  Non-relevant pairs (i e  negative training examples) consist of news articles that contain an entity but are not referenced in that entity's page  If a news article INLINEFORM1 is referred from INLINEFORM2 at year INLINEFORM3 , the features are computed taking into account the entity profiles at year INLINEFORM4  
Article-Section Ground-truth  The dataset consists of the triple INLINEFORM0 , where INLINEFORM1 , where we assume that INLINEFORM2 has already been determined as relevant  We therefore have a multi-class classification problem where we need to determine the section of INLINEFORM3 where INLINEFORM4 is cited  Similar to the article-entity ground truth, here too the features compute the similarity between INLINEFORM5 , INLINEFORM6 and INLINEFORM7  
Data Pre-Processing
We POS-tag the news articles and entity profiles INLINEFORM0 with the Stanford tagger BIBREF21   For entity linking the news articles, we use TagMe! BIBREF22 with a confidence score of 0 3  On a manual inspection of a random sample of 1000 disambiguated entities, the accuracy is above 0 9  On average, the number of entities per news article is approximately 30  For entity linking the entity profiles, we simply follow the anchor text that refers to Wikipedia entities 
Train and Testing Evaluation Setup
We evaluate the generated supervised models for the two tasks, AEP and ASP, by splitting the train and testing instances  It is important to note that for the pairs INLINEFORM0 and the triple INLINEFORM1 , the news article INLINEFORM2 is referenced at time INLINEFORM3 by entity INLINEFORM4 , while the features take into account the entity profile at time INLINEFORM5   This avoids any `overlapping' content between the news article and the entity page, which could affect the learning task of the functions INLINEFORM6 and INLINEFORM7   Table TABREF47 shows the statistics of train and test instances  We learn the functions at year INLINEFORM8 and test on instances for the years greater than INLINEFORM9   Please note that we do not show the performance for year 2014 as we do not have data for 2015 for evaluation 
Article-Section Placement
Here we show the evaluation setup for ASP task and discuss the results with a focus on three main aspects, (i) the overall performance across the years, (ii) the entity class specific performance, and (iii) the impact on entity profile expansion by suggesting missing sections to entities based on the pre-computed templates 
Baselines  To the best of our knowledge, we are not aware of any comparable approach for this task  Therefore, the baselines we consider are the following:
S1: Pick the section from template INLINEFORM0 with the highest lexical similarity to INLINEFORM1 : S1 INLINEFORM2
S2: Place the news into the most frequent section in INLINEFORM0
Learning Models  We use Random Forests (RF) BIBREF23 and Support Vector Machines (SVM) BIBREF24   The models are optimized taking into account the features in Table TABREF31   In contrast to the AEP task, here the scale of the number of instances allows us to learn the SVM models  The SVM model is optimized using the INLINEFORM0 loss function and uses the Gaussian kernels 
Metrics  We compute precision P as the ratio of news for which we pick a section INLINEFORM0 from INLINEFORM1 and INLINEFORM2 conforms to the one in our ground-truth (see Section SECREF40 )  The definition of recall R and F1 score follows from that of precision 
Figure FIGREF66 shows the overall performance and a comparison of our approach (when INLINEFORM0 is optimized using SVM) against the best performing baseline S2  With the increase in the number of training instances for the ASP task the performance is a monotonically non-decreasing function  For the year 2009, we optimize the learning objective of INLINEFORM1 with around 8% of the total instances, and evaluate on the rest  The performance on average is around P=0 66 across all classes  Even though for many classes the performance is already stable (as we will see in the next section), for some classes we improve further  If we take into account the years between 2010 and 2012, we have an increase of INLINEFORM2 P=0 17, with around 70% of instances used for training and the remainder for evaluation  For the remaining years the total improvement is INLINEFORM3 P=0 18 in contrast to the performance at year 2009 
On the other hand, the baseline S1 has an average precision of P=0 12  The performance across the years varies slightly, with the year 2011 having the highest average precision of P=0 13  Always picking the most frequent section as in S2, as shown in Figure FIGREF66 , results in an average precision of P=0 17, with a uniform distribution across the years 
Here we show the performance of INLINEFORM0 decomposed for the different entity classes  Specifically we analyze the 27 classes in Figure FIGREF42   In Table TABREF68 , we show the results for a range of years (we omit showing all years due to space constraints)  For illustration purposes only, we group them into four main classes ( INLINEFORM1 Person, Organization, Location, Event INLINEFORM2 ) and into the specific sub-classes shown in the second column in Table TABREF68   For instance, the entity classes OfficeHolder and Politician are aggregated into Person–Politics 
It is evident that in the first year the performance is lower in contrast to the later years  This is due to the fact that as we proceed, we can better generalize and accurately determine the correct fit of an article INLINEFORM0 into one of the sections from the pre-computed templates INLINEFORM1   The results are already stable for the year range INLINEFORM2   For a few Person sub-classes, e g  Politics, Entertainment, we achieve an F1 score above 0 9  These additionally represent classes with a sufficient number of training instances for the years INLINEFORM3   The lowest F1 score is for the Criminal and Television classes  However, this is directly correlated with the insufficient number of instances 
The baseline approaches for the ASP task perform poorly  S1, based on lexical similarity, has a varying performance for different entity classes  The best performance is achieved for the class Person – Politics, with P=0 43  This highlights the importance of our feature choice and that the ASP cannot be considered as a linear function, where the maximum similarity yields the best results  For different entity classes different features and combination of features is necessary  Considering that S2 is the overall best performing baseline, through our approach INLINEFORM0 we have a significant improvement of over INLINEFORM1 P=+0 64 
The models we learn are very robust and obtain high accuracy, fulfilling our pre-condition for accurate news suggestions into the entity sections  We measure the robustness of INLINEFORM0 through the INLINEFORM1 statistic  In this case, we have a model with roughly 10 labels (corresponding to the number of sections in a template INLINEFORM2 )  The score we achieve shows that our model predicts with high confidence with INLINEFORM3  
The last analysis is the impact we have on expanding entity profiles INLINEFORM0 with new sections  Figure FIGREF70 shows the ratio of sections for which we correctly suggest an article INLINEFORM1 to the right section in the section template INLINEFORM2   The ratio here corresponds to sections that are not present in the entity profile at year INLINEFORM3 , that is INLINEFORM4   However, given the generated templates INLINEFORM5 , we can expand the entity profile INLINEFORM6 with a new section at time INLINEFORM7   In details, in the absence of a section at time INLINEFORM8 , our model trains well on similar sections from the section template INLINEFORM9 , hence we can predict accurately the section and in this case suggest its addition to the entity profile  With time, it is obvious that the expansion rate decreases at later years as the entity profiles become more `complete' 
This is particularly interesting for expanding the entity profiles of long-tail entities as well as updating entities with real-world emerging events that are added constantly  In many cases such missing sections are present at one of the entities of the respective entity class INLINEFORM0   An obvious case is the example taken in Section SECREF16 , where the `Accidents' is rather common for entities of type Airline  However, it is non-existent for some specific entity instances, i e Germanwings airline 
Through our ASP approach INLINEFORM0 , we are able to expand both long-tail and trunk entities  We distinguish between the two types of entities by simply measuring their section text length  The real distribution in the ground truth (see Section SECREF40 ) is 27% and 73% are long-tail and trunk entities, respectively  We are able to expand the entity profiles for both cases and all entity classes without a significant difference, with the only exception being the class Creative Work, where we expand significantly more trunk entities 
Conclusion and Future Work
In this work, we have proposed an automated approach for the novel task of suggesting news articles to Wikipedia entity pages to facilitate Wikipedia updating  The process consists of two stages  In the first stage, article–entity placement, we suggest news articles to entity pages by considering three main factors, such as entity salience in a news article, relative authority and novelty of news articles for an entity page  In the second stage, article–section placement, we determine the best fitting section in an entity page  Here, we remedy the problem of incomplete entity section profiles by constructing section templates for specific entity classes  This allows us to add missing sections to entity pages  We carry out an extensive experimental evaluation on 351,983 news articles and 73,734 entities coming from 27 distinct entity classes  For the first stage, we achieve an overall performance with P=0 93, R=0 514 and F1=0 676, outperforming our baseline competitors significantly  For the second stage, we show that we can learn incrementally to determine the correct section for a news article based on section templates  The overall performance across different classes is P=0 844, R=0 885 and F1=0 860 
In the future, we will enhance our work by extracting facts from the suggested news articles  Results suggest that the news content cited in entity pages comes from the first paragraphs  However, challenging task such as the canonicalization and chronological ordering of facts, still remain 
Context 3: Introduction
Word Sense Disambiguation (WSD) is a fundamental task and long-standing challenge in Natural Language Processing (NLP), which aims to find the exact sense of an ambiguous word in a particular context BIBREF0  Previous WSD approaches can be grouped into two main categories: knowledge-based and supervised methods 
Knowledge-based WSD methods rely on lexical resources like WordNet BIBREF1 and usually exploit two kinds of lexical knowledge  The gloss, which defines a word sense meaning, is first utilized in Lesk algorithm BIBREF2 and then widely taken into account in many other approaches BIBREF3, BIBREF4  Besides, structural properties of semantic graphs are mainly used in graph-based algorithms BIBREF5, BIBREF6 
Traditional supervised WSD methods BIBREF7, BIBREF8, BIBREF9 focus on extracting manually designed features and then train a dedicated classifier (word expert) for every target lemma 
Although word expert supervised WSD methods perform better, they are less flexible than knowledge-based methods in the all-words WSD task BIBREF10  Recent neural-based methods are devoted to dealing with this problem  BIBREF11 present a supervised classifier based on Bi-LSTM, which shares parameters among all word types except the last layer  BIBREF10 convert WSD task to a sequence labeling task, thus building a unified model for all polysemous words  However, neither of them can totally beat the best word expert supervised methods 
More recently, BIBREF12 propose to leverage the gloss information from WordNet and model the semantic relationship between the context and gloss in an improved memory network  Similarly, BIBREF13 introduce a (hierarchical) co-attention mechanism to generate co-dependent representations for the context and gloss  Their attempts prove that incorporating gloss knowledge into supervised WSD approach is helpful, but they still have not achieved much improvement, because they may not make full use of gloss knowledge 
In this paper, we focus on how to better leverage gloss information in a supervised neural WSD system  Recently, the pre-trained language models, such as ELMo BIBREF14 and BERT BIBREF15, have shown their effectiveness to alleviate the effort of feature engineering  Especially, BERT has achieved excellent results in question answering (QA) and natural language inference (NLI)  We construct context-gloss pairs from glosses of all possible senses (in WordNet) of the target word, thus treating WSD task as a sentence-pair classification problem  We fine-tune the pre-trained BERT model and achieve new state-of-the-art results on WSD task  In particular, our contribution is two-fold:
1  We construct context-gloss pairs and propose three BERT-based models for WSD 
2  We fine-tune the pre-trained BERT model, and the experimental results on several English all-words WSD benchmark datasets show that our approach significantly outperforms the state-of-the-art systems 
Methodology
In this section, we describe our method in detail 
Methodology ::: Task Definition
In WSD, a sentence $s$ usually consists of a series of words: $\lbrace w_1,\cdots ,w_m\rbrace $, and some of the words $\lbrace w_{i_1},\cdots ,w_{i_k}\rbrace $ are targets $\lbrace t_1,\cdots ,t_k\rbrace $ need to be disambiguated  For each target $t$, its candidate senses $\lbrace c_1,\cdots ,c_n\rbrace $ come from entries of its lemma in a pre-defined sense inventory (usually WordNet)  Therefore, WSD task aims to find the most suitable entry (symbolized as unique sense key) for each target in a sentence  See a sentence example in Table TABREF1 
Methodology ::: BERT
BERT BIBREF15 is a new language representation model, and its architecture is a multi-layer bidirectional Transformer encoder  BERT model is pre-trained on a large corpus and two novel unsupervised prediction tasks, i e , masked language model and next sentence prediction tasks are used in pre-training  When incorporating BERT into downstream tasks, the fine-tuning procedure is recommended  We fine-tune the pre-trained BERT model on WSD task 
Methodology ::: BERT ::: BERT(Token-CLS)
Since every target in a sentence needs to be disambiguated to find its exact sense, WSD task can be regarded as a token-level classification task  To incorporate BERT to WSD task, we take the final hidden state of the token corresponding to the target word (if more than one token, we average them) and add a classification layer for every target lemma, which is the same as the last layer of the Bi-LSTM model BIBREF11 
Methodology ::: GlossBERT
BERT can explicitly model the relationship of a pair of texts, which has shown to be beneficial to many pair-wise natural language understanding tasks  In order to fully leverage gloss information, we propose GlossBERT to construct context-gloss pairs from all possible senses of the target word in WordNet, thus treating WSD task as a sentence-pair classification problem 
We describe our construction method with an example (See Table TABREF1)  There are four targets in this sentence, and here we take target word research as an example:
Methodology ::: GlossBERT ::: Context-Gloss Pairs
The sentence containing target words is denoted as context sentence  For each target word, we extract glosses of all $N$ possible senses (here $N=4$) of the target word (research) in WordNet to obtain the gloss sentence  [CLS] and [SEP] marks are added to the context-gloss pairs to make it suitable for the input of BERT model  A similar idea is also used in aspect-based sentiment analysis BIBREF16 
Methodology ::: GlossBERT ::: Context-Gloss Pairs with Weak Supervision
Based on the previous construction method, we add weak supervised signals to the context-gloss pairs (see the highlighted part in Table TABREF1)  The signal in the gloss sentence aims to point out the target word, and the signal in the context sentence aims to emphasize the target word considering the situation that a target word may occur more than one time in the same sentence 
Therefore, each target word has $N$ context-gloss pair training instances ($label\in \lbrace yes, no\rbrace $)  When testing, we output the probability of $label=yes$ of each context-gloss pair and choose the sense corresponding to the highest probability as the prediction label of the target word  We experiment with three GlossBERT models:
Methodology ::: GlossBERT ::: GlossBERT(Token-CLS)
We use context-gloss pairs as input  We highlight the target word by taking the final hidden state of the token corresponding to the target word (if more than one token, we average them) and add a classification layer ($label\in \lbrace yes, no\rbrace $) 
Methodology ::: GlossBERT ::: GlossBERT(Sent-CLS)
We use context-gloss pairs as input  We take the final hidden state of the first token [CLS] as the representation of the whole sequence and add a classification layer ($label\in \lbrace yes, no\rbrace $), which does not highlight the target word 
Methodology ::: GlossBERT ::: GlossBERT(Sent-CLS-WS)
We use context-gloss pairs with weak supervision as input  We take the final hidden state of the first token [CLS] and add a classification layer ($label\in \lbrace yes, no\rbrace $), which weekly highlight the target word by the weak supervision 
Experiments ::: Datasets
The statistics of the WSD datasets are shown in Table TABREF12 
Experiments ::: Datasets ::: Training Dataset
Following previous work BIBREF13, BIBREF12, BIBREF10, BIBREF17, BIBREF9, BIBREF7, we choose SemCor3 0 as training corpus, which is the largest corpus manually annotated with WordNet sense for WSD 
Experiments ::: Datasets ::: Evaluation Datasets
We evaluate our method on several English all-words WSD datasets  For a fair comparison, we use the benchmark datasets proposed by BIBREF17 which include five standard all-words fine-grained WSD datasets from the Senseval and SemEval competitions: Senseval-2 (SE2), Senseval-3 (SE3), SemEval-2007 (SE07), SemEval-2013 (SE13) and SemEval-2015 (SE15)  Following BIBREF13, BIBREF12 and BIBREF10, we choose SE07, the smallest among these test sets, as the development set 
Experiments ::: Datasets ::: WordNet
Since BIBREF17 map all the sense annotations in these datasets from their original versions to WordNet 3 0, we extract word sense glosses from WordNet 3 0 
Experiments ::: Settings
We use the pre-trained uncased BERT$_\mathrm {BASE}$ model for fine-tuning, because we find that BERT$_\mathrm {LARGE}$ model performs slightly worse than BERT$_\mathrm {BASE}$ in this task  The number of Transformer blocks is 12, the number of the hidden layer is 768, the number of self-attention heads is 12, and the total number of parameters of the pre-trained model is 110M  When fine-tuning, we use the development set (SE07) to find the optimal settings for our experiments  We keep the dropout probability at 0 1, set the number of epochs to 4  The initial learning rate is 2e-5, and the batch size is 64 
Experiments ::: Results
Table TABREF19 shows the performance of our method on the English all-words WSD benchmark datasets  We compare our approach with previous methods 
The first block shows the MFS baseline, which selects the most frequent sense in the training corpus for each target word 
The second block shows two knowledge-based systems  Lesk$_{ext+emb}$ BIBREF4 is a variant of Lesk algorithm BIBREF2 by calculating the gloss-context overlap of the target word  Babelfy BIBREF6 is a unified graph-based approach which exploits the semantic network structure from BabelNet 
The third block shows two word expert traditional supervised systems  IMS BIBREF7 is a flexible framework which trains SVM classifiers and uses local features  And IMS$_{+emb}$ BIBREF9 is the best configuration of the IMS framework, which also integrates word embeddings as features 
The fourth block shows several recent neural-based methods  Bi-LSTM BIBREF11 is a baseline for neural models  Bi-LSTM$_{+ att  + LEX + POS}$ BIBREF10 is a multi-task learning framework for WSD, POS tagging, and LEX with self-attention mechanism, which converts WSD to a sequence learning task  GAS$_{ext}$ BIBREF12 is a variant of GAS which is a gloss-augmented variant of the memory network by extending gloss knowledge  CAN$^s$ and HCAN BIBREF13 are sentence-level and hierarchical co-attention neural network models which leverage gloss knowledge 
In the last block, we report the performance of our method  BERT(Token-CLS) is our baseline, which does not incorporate gloss information, and it performs slightly worse than previous traditional supervised methods and recent neural-based methods  It proves that directly using BERT cannot obtain performance growth  The other three methods outperform other models by a substantial margin, which proves that the improvements come from leveraging BERT to better exploit gloss information  It is worth noting that our method achieves significant improvements in SE07 and Verb over previous methods, which have the highest ambiguity level among all datasets and all POS tags respectively according to BIBREF17 
Moreover, GlossBERT(Token-CLS) performs better than GlossBERT(Sent-CLS), which proves that highlighting the target word in the sentence is important  However, the weakly highlighting method GlossBERT(Sent-CLS-WS) performs best in most circumstances, which may result from its combination of the advantages of the other two methods 
Experiments ::: Discussion
There are two main reasons for the great improvements of our experimental results  First, we construct context-gloss pairs and convert WSD problem to a sentence-pair classification task which is similar to NLI tasks and train only one classifier, which is equivalent to expanding the corpus  Second, we leverage BERT BIBREF15 to better exploit the gloss information  BERT model shows its advantage in dealing with sentence-pair classification tasks by its amazing improvement on QA and NLI tasks  This advantage comes from both of its two novel unsupervised prediction tasks 
Compared with traditional word expert supervised methods, our GlossBERT shows its effectiveness to alleviate the effort of feature engineering and does not require training a dedicated classifier for every target lemma  Up to now, it can be said that the neural network method can totally beat the traditional word expert method  Compared with recent neural-based methods, our solution is more intuitive and can make better use of gloss knowledge  Besides, our approach demonstrates that when we fine-tune BERT on a downstream task, converting it into a sentence-pair classification task may be a good choice 
Conclusion
In this paper, we seek to better leverage gloss knowledge in a supervised neural WSD system  We propose a new solution to WSD by constructing context-gloss pairs and then converting WSD to a sentence-pair classification task  We fine-tune the pre-trained BERT model and achieve new state-of-the-art results on WSD task 
Acknowledgments
We would like to thank the anonymous reviewers for their valuable comments  The research work is supported by National Natural Science Foundation of China (No  61751201 and 61672162), Shanghai Municipal Science and Technology Commission (16JC1420401 and 17JC1404100), Shanghai Municipal Science and Technology Major Project (No 2018SHZDZX01) and ZJLab 

ground_truth: For Article-Entity placement, they consider two baselines: the first one using only salience-based features, and the second baseline checks if the entity appears in the title of the article. 

For Article-Section Placement, they consider two baselines: the first picks the section with the highest lexical similarity to the article, and the second one picks the most frequent section.

B1. The first baseline uses only the salience-based features by Dunietz and Gillick BIBREF11 ., B2. The second baseline assigns the value relevant to a pair INLINEFORM0 , if and only if INLINEFORM1 appears in the title of INLINEFORM2 .

, S1: Pick the section from template INLINEFORM0 with the highest lexical similarity to INLINEFORM1 : S1 INLINEFORM2, S2: Place the news into the most frequent section in INLINEFORM0

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. Do they use large or small BERT?
Context 4:  We used the following global network properties:
Number of Strongly Connected Components (SCC): a Strongly Connected Component of a directed graph is a maximal (sub)graph where for each pair of vertices $u,v$ there is a path in each direction ($u\rightarrow v$, $v\rightarrow u$) 
Size of the Largest Strongly Connected Component (LSCC): the number of nodes in the largest strongly connected component of a given graph 
Number of Weakly Connected Components (WCC): a Weakly Connected Component of a directed graph is a maximal (sub)graph where for each pair of vertices $(u, v)$ there is a path $u \leftrightarrow v$ ignoring edge directions 
Size of the Largest Weakly Connected Component (LWCC): the number of nodes in the largest weakly connected component of a given graph 
Diameter of the Largest Weakly Connected Component (DWCC): the largest distance (length of the shortest path) between two nodes in the (undirected version of) largest weakly connected component of a graph 
Average Clustering Coefficient (CC): the average of the local clustering coefficients of all nodes in a graph; the local clustering coefficient of a node quantifies how close its neighbours are to being a complete graph (or a clique)  It is computed according to BIBREF28 
Main K-core Number (KC): a K-core BIBREF13 of a graph is a maximal sub-graph that contains nodes of internal degree $k$ or more; the main K-core number is the highest value of $k$ (in directed graphs the total degree is considered) 
Density (d): the density for directed graphs is $d=\frac{|E|}{|V||V-1|}$, where $|E|$ is the number of edges and $|N|$ is the number of vertices in the graph; the density equals 0 for a graph without edges and 1 for a complete graph 
Structural virality of the largest weakly connected component (SV): this measure is defined in BIBREF14 as the average distance between all pairs of nodes in a cascade tree or, equivalently, as the average depth of nodes, averaged over all nodes in turn acting as a root; for $|V| > 1$ vertices, $SV=\frac{1}{|V||V-1|}\sum _i\sum _j d_{ij}$ where $d_{ij}$ denotes the length of the shortest path between nodes $i$ and $j$  This is equivalent to compute the Wiener's index BIBREF29 of the graph and multiply it by a factor $\frac{1}{|V||V-1|}$  In our case we computed it for the undirected equivalent graph of the largest weakly connected component, setting it to 0 whenever $V=1$ 
We used networkx Python package BIBREF30 to compute all features  Whenever a layer is empty  we simply set to 0 all its features  In addition to computing the above nine features for each layer, we added two indicators for encoding information about pure tweets, namely the number T of pure tweets (containing URLs to a given news article) and the number U of unique users authoring those tweets  Therefore, a single diffusion network is represented by a vector with $9\cdot 4+2=38$ entries 
Methodology ::: Interpretation of network features and layers
Aforementioned network properties can be qualitatively explained in terms of social footprints as follows: SCC correlates with the size of the diffusion network, as the propagation of news occurs in a broadcast manner most of the time, i e  re-tweets dominate on other interactions, while LSCC allows to distinguish cases where such mono-directionality is somehow broken  WCC equals (approximately) the number of distinct diffusion cascades pertaining to each news article, with exceptions corresponding to those cases where some cascades merge together via Twitter interactions such as mentions, quotes and replies, and accordingly LWCC and DWCC equals the size and the depth of the largest cascade  CC corresponds to the level of connectedness of neighboring users in a given diffusion network whereas KC identifies the set of most influential users in a network and describes the efficiency of information spreading BIBREF17  Finally, d describes the proportions of potential connections between users which are actually activated and SV indicates whether a news item has gained popularity with a single and large broadcast or in a more viral fashion through multiple generations 
For what concerns different Twitter actions, users primarily interact with each other using retweets and mentions BIBREF20 
The former are the main engagement activity and act as a form of endorsement, allowing users to rebroadcast content generated by other users BIBREF31  Besides, when node B retweets node A we have an implicit confirmation that information from A appeared in B's Twitter feed BIBREF12  Quotes are simply a special case of retweets with comments 
Mentions usually include personal conversations as they allow someone to address a specific user or to refer to an individual in the third person; in the first case they are located at the beginning of a tweet and they are known as replies, otherwise they are put in the body of a tweet BIBREF20  The network of mentions is usually seen as a stronger version of interactions between Twitter users, compared to the traditional graph of follower/following relationships BIBREF32 
Experiments ::: Setup
We performed classification experiments using a basic off-the-shelf classifier, namely Logistic Regression (LR) with L2 penalty; this also allows us to compare results with our baseline  We applied a standardization of the features and we used the default configuration for parameters as described in scikit-learn package BIBREF33  We also tested other classifiers (such as K-Nearest Neighbors, Support Vector Machines and Random Forest) but we omit results as they give comparable performances  We remark that our goal is to show that a very simple machine learning framework, with no parameter tuning and optimization, allows for accurate results with our network-based approach 
We used the following evaluation metrics to assess the performances of different classifiers (TP=true positives, FP=false positives, FN=false negatives):
Precision = $\frac{TP}{TP+FP}$, the ability of a classifier not to label as positive a negative sample 
Recall = $\frac{TP}{TP+FN}$, the ability of a classifier to retrieve all positive samples 
F1-score = $2 \frac{\mbox{Precision} \cdot \mbox{Recall}}{\mbox{Precision} + \mbox{Recall}}$, the harmonic average of Precision and Recall 
Area Under the Receiver Operating Characteristic curve (AUROC); the Receiver Operating Characteristic (ROC) curve BIBREF34, which plots the TP rate versus the FP rate, shows the ability of a classifier to discriminate positive samples from negative ones as its threshold is varied; the AUROC value is in the range $[0, 1]$, with the random baseline classifier holding AUROC$=0 5$ and the ideal perfect classifier AUROC$=1$; thus larger AUROC values (and steeper ROCs) correspond to better classifiers 
In particular we computed so-called macro average–simple unweighted mean–of these metrics evaluated considering both labels (disinformation and mainstream)  We employed stratified shuffle split cross validation (with 10 folds) to evaluate performances 
Finally, we partitioned networks according to the total number of unique users involved in the sharing, i e  the number of nodes in the aggregated network represented with a single-layer representation considering together all layers and also pure tweets  A breakdown of both datasets according to size class (and political biases for the US scenario) is provided in Table 1 and Table 2 
Experiments ::: Classification performances
In Table 3 we first provide classification performances on the US dataset for the LR classifier evaluated on the size class described in Table 1  We can observe that in all instances our methodology performs better than a random classifier (50% AUROC), with AUROC values above 85% in all cases 
For what concerns political biases, as the classes of mainstream and disinformation networks are not balanced (e g , 1,292 mainstream and 4,149 disinformation networks with right bias) we employ a Balanced Random Forest with default parameters (as provided in imblearn Python package BIBREF35)  In order to test the robustness of our methodology, we trained only on left-biased networks or right-biased networks and tested on the entire set of sources (relative to the US dataset); we provide a comparison of AUROC values for both biases in Figure 4  We can notice that our multi-layer approach still entails significant results, thus showing that it can accurately distinguish mainstream news from disinformation regardless of the political bias  We further corroborated this result with additional classification experiments, that show similar performances, in which we excluded from the training/test set two specific sources (one at a time and both at the same time) that outweigh the others in terms of data samples–respectively "breitbart com" for right-biased sources and "politicususa com" for left-biased ones 
We performed classification experiments on the Italian dataset using the LR classifier and different size classes (we excluded $[1000, +\infty )$ which is empty); we show results for different evaluation metrics in Table 3  We can see that despite the limited number of samples (one order of magnitude smaller than the US dataset) the performances are overall in accordance with the US scenario  As shown in Table 4, we obtain results which are much better than our baseline in all size classes (see Table 4):
In the US dataset our multi-layer methodology performs much better in all size classes except for large networks ($[1000, +\infty )$ size class), reaching up to 13% improvement on smaller networks ($[0, 100)$ size class);
In the IT dataset our multi-layer methodology outperforms the baseline in all size classes, with the maximum performance gain (20%) on medium networks ($[100, 1000)$ size class); the baseline generally reaches bad performances compared to the US scenario 
Overall, our performances are comparable with those achieved by two state-of-the-art deep learning models for "fake news" detection BIBREF9BIBREF36 
Experiments ::: Layer importance analysis
In order to understand the impact of each layer on the performances of classifiers, we performed additional experiments considering separately each layer (we ignored T and U features relative to pure tweets)  In Table 5 we show metrics for each layer and all size classes, computed with a 10-fold stratified shuffle split cross validation, evaluated on the US dataset; in Figure 5 we show AUROC values for each layer compared with the general multi-layer approach  We can notice that both Q and M layers alone capture adequately the discrepancies of the two distinct news domains in the United States as they obtain good results with AUROC values in the range 75%-86%; these are comparable with those of the multi-layer approach which, nevertheless, outperforms them across all size classes 
We obtained similar performances for the Italian dataset, as the M layer obtains comparable performances w r t multi-layer approach with AUROC values in the range 72%-82%  We do not show these results for sake of conciseness 
Experiments ::: Feature importance analysis
We further investigated the importance of each feature by performing a $\chi ^2$ test, with 10-fold stratified shuffle split cross validation, considering the entire range of network sizes $[0, +\infty )$  We show the Top-5 most discriminative features for each country in Table 6 
We can notice the exact same set of features (with different relative orderings in the Top-3) in both countries; these correspond to two global network propertie–LWCC, which indicates the size of the largest cascade in the layer, and SCC, which correlates with the size of the network–associated to the same set of layers (Quotes, Retweets and Mentions) 
We further performed a $\chi ^2$ test to highlight the most discriminative features in the M layer of both countries, which performed equally well in the classification task as previously highlighted; also in this case we focused on the entire range of network sizes $[0, +\infty )$  Interestingly, we discovered exactly the same set of Top-3 features in both countries, namely LWCC, SCC and DWCC (which indicates the depth of the largest cascade in the layer) 
An inspection of the distributions of all aforementioned features revealed that disinformation news exhibit on average larger values than mainstream news 
We can qualitatively sum up these results as follows:
Sharing patterns in the two news domains exhibit discrepancies which might be country-independent and due to the content that is being shared 
Interactions in disinformation sharing cascades tends to be broader and deeper than in mainstream news, as widely reported in the literature BIBREF8BIBREF2BIBREF7 
Users likely make a different usage of mentions when sharing news belonging to the two domains, consequently shaping different sharing patterns 
Experiments ::: Temporal analysis
Similar to BIBREF9, we carried out additional experiments to answer the following question: how long do we need to observe a news spreading on Twitter in order to accurately classify it as disinformation or mainstream?
With this goal, we built several versions of our original dataset of multi-layer networks by considering in turn the following lifetimes: 1 hour, 6 hours, 12 hours, 1 day, 2 days, 3 days and 7 days; for each case, we computed the global network properties of the corresponding network and evaluated the LR classifier with 10-fold cross validation, separately for each lifetime (and considering always the entire set of networks)  We show corresponding AUROC values for both US and IT datasets in Figure 6 
We can see that in both countries news diffusion networks can be accurately classified after just a few hours of spreading, with AUROC values which are larger than 80% after only 6 hours of diffusion  These results are very promising and suggest that articles pertaining to the two news domains exhibit discrepancies in their sharing patterns that can be timely exploited in order to rapidly detect misleading items from factual information 
Conclusions
In this work we tackled the problem of the automatic classification of news articles in two domains, namely mainstream and disinformation news, with a language-independent approach which is based solely on the diffusion of news items on Twitter social platform  We disentangled different types of interactions on Twitter to accordingly build a multi-layer representation of news diffusion networks, and we computed a set of global network properties–separately for each layer–in order to encode each network with a tuple of features  Our goal was to investigate whether a multi-layer representation performs better than one layer BIBREF11, and to understand which of the features, observed at given layers, are most effective in the classification task 
Experiments with an off-the-shelf classifier such as Logistic Regression on datasets pertaining to two different media landscapes (US and Italy) yield very accurate classification results (AUROC up to 94%), even when accounting for the different political bias of news sources, which are far better than our baseline BIBREF11 with improvements up to 20%  Classification performances using single layers show that the layer of mentions alone entails better performance w r t other layers in both countries 
We also highlighted the most discriminative features across different layers in both countries; the results suggest that differences between the two news domains might be country-independent but rather due only to the typology of content shared, and that disinformation news shape broader and deeper cascades 
Additional experiments involving the temporal evolution of Twitter diffusion networks show that our methodology can accurate classify mainstream and disinformation news after a few hours of propagation on the platform 
Overall, our results prove that the topological features of multi-layer diffusion networks might be effectively exploited to detect online disinformation  We do not deny the presence of deceptive efforts to orchestrate the regular spread of information on social media via content amplification and manipulation BIBREF37BIBREF38  On the contrary, we postulate that such hidden forces might play to accentuate the discrepancies between the diffusion patterns of disinformation and mainstream news (and thus to make our methodology effective) 
In the future we aim to further investigate three directions: (1) employ temporal networks to represent news diffusion and apply classification techniques that take into account the sequential aspect of data (e g  recurrent neural networks); (2) carry out an extensive comparison of the diffusion of disinformation and mainstream news across countries to investigate deeper the presence of differences and similarities in sharing patterns; (3) leverage our network-based features in addition to state-of-the-art text-based approaches for "fake news" dete ction in order to deliver a real-world system to detect misleading and harmful information spreading on social media 

ground_truth: small BERT

small BERT