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 was the previous state of the art for this task?
Context 1: Introduction
Massive Open Online Courses (MOOCs) have strived to bridge the social gap in higher education by bringing quality education from reputed universities to students at large  Such massive scaling through online classrooms, however, disrupt co-located, synchronous two-way communication between the students and the instructor 
MOOC platforms provide discussion forums for students to talk to their classmates about the lectures, homeworks, quizzes and provide a venue to socialise  Instructors (defined here as the course instructors, their teaching assistants and the MOOC platform's technical staff) monitor the discussion forum to post (reply to their message) in discussion threads among students  We refer to this posting as intervention, following prior work BIBREF0   However, due to large student enrolment, the student–instructor ratio in MOOCs is very high Therefore, instructors are not able to monitor and participate in all student discussions  To address this problem, a number of works have proposed systems e g , BIBREF0 , BIBREF1 to aid instructors to selectively intervene on student discussions where they are needed the most 
In this paper, we improve the state-of-the-art for instructor intervention in MOOC forums  We propose the first neural models for this prediction problem  We show that modelling the thread structure and the sequence of posts explicitly improves performance  Instructors in different MOOCs from different subject areas intervene differently  For example, on a Science, Technology, Engineering and Mathematics (STEM) MOOC, instructors may often intervene early as possible to resolve misunderstanding of the subject material and prevent confusion  However, in a Humanities MOOC, instructors allow for the students to explore open-ended discussions and debate among themselves  Such instructors may prefer to intervene later in the discussion to encourage further discussion or resolve conflicts among students  We therefore propose attention models to infer the latent context, i e , the series of posts that trigger an intervention  Earlier studies on MOOC forum intervention either model the entire context or require the context size to be specified explicitly 
Problem Statement
A thread INLINEFORM0 consists of a series of posts INLINEFORM1 through INLINEFORM2 where INLINEFORM3 is an instructor's post when INLINEFORM4 is intervened, if applicable  INLINEFORM5 is considered intervened if an instructor had posted at least once  The problem of predicting instructor intervention is cast as a binary classification problem  Intervened threads are predicted as 1 given while non-intervened threads are predicted as 0 given posts INLINEFORM6 through INLINEFORM7  
The primary problem leads to a secondary problem of inferring the appropriate amount of context to intervene  We define a context INLINEFORM0 of a post INLINEFORM1 as a series of linear contiguous posts INLINEFORM2 through INLINEFORM3 where INLINEFORM4   The problem of inferring context is to identify context INLINEFORM5 from a set of candidate contexts INLINEFORM6  
Modelling Context in Forums
Context has been used and modelled in various ways for different problems in discussion forums  In a work on a closely related problem of forum thread retrieval BIBREF2 models context using inter-post discourse e g , Question-Answer  BIBREF3 models the structural dependencies and relationships between forum posts using a conditional random field in their problem to infer the reply structure  Unlike BIBREF2 , BIBREF3 can be used to model any structural dependency and is, therefore, more general  In this paper, we seek to infer general dependencies between a reply and its previous context whereas BIBREF3 inference is limited to pairs of posts  More recently BIBREF4 proposed a context based model which factorises attention over threads of different lengths  Differently, we do not model length but the context before a post  However, our attention models cater to threads of all lengths 
BIBREF5 proposed graph structured LSTM to model the explicit reply structure in Reddit forums  Our work does not assume access to such a reply structure because 1) Coursera forums do not provide one and 2) forum participants often err by posting their reply to a different post than that they intended  At the other end of the spectrum are document classification models that do not assume structure in the document layout but try to infer inherent structure in the natural language, viz, words, sentences, paragraphs and documents  Hierarchical attention BIBREF6 is a well know recent work that classifies documents using a multi-level LSTMs with attention mechanism to select important units at each hierarchical level  Differently, we propose a hierarchical model that encodes layout hierarchy between a post and a thread but also infers reply structure using a attention mechanism since the layout does not reliably encode it 
Instructor Intervention in MOOC forums
The problem of predicting instructor intervention in MOOCs was proposed by BIBREF0   Later BIBREF7 evaluated baseline models by BIBREF0 over a larger corpus and found the results to vary widely across MOOCs  Since then subsequent works have used similar diverse evaluations on the same prediction problem BIBREF1 , BIBREF8   BIBREF1 proposed models with discourse features to enable better prediction over unseen MOOCs  BIBREF8 recently showed interventions on Coursera forums to be biased by the position at which a thread appears to an instructor viewing the forum interface and proposed methods for debiased prediction 
While all works since BIBREF0 address key limitations in this line of research, they have not investigated the role of structure and sequence in the threaded discussion in predicting instructor interventions  BIBREF0 proposed probabilistic graphical models to model structure and sequence  They inferred vocabulary dependent latent post categories to model the thread sequence and infer states that triggered intervention  Their model, however, requires a hyperparameter for the number of latent states  It is likely that their empirically reported setting will not generalise due to their weak evaluation BIBREF7   In this paper, we propose models to infer the context that triggers instructor intervention that does not require context lengths to be set apriori  All our proposed models generalise over modelling assumptions made by BIBREF0  
For the purpose of comparison against a state-of-the-art and competing baselines we choose BIBREF7 since BIBREF0 's system and data are not available for replication 
Data and Preprocessing
We evaluate our proposed models over a corpus of 12 MOOC iterations (offerings) on Coursera org In partnership with Coursera and in line with its Terms of Service, we obtained the data for use in our academic research  Following prior work BIBREF7 we evaluate over a diverse dataset to represent MOOCs of varying sizes, instructor styles, instructor team sizes and number of threads intervened  We only include threads from sub-forums on Lecture, Homework, Quiz and Exam  We also normalise and label sub-forums with other non-standard names (e g , Assignments instead of Homework) into of the four said sub-forums  Threads on general discussion, meet and greet and other custom sub-forums for social chitchat are omitted as our focus is to aid instructors on intervening on discussion on the subject matter  We also exclude announcement threads and other threads started by instructors since they are not interventions  We preprocess each thread by replacing URLs, equations and other mathematical formulae and references to timestamps in lecture videos by tokens INLINEFORM0 URL INLINEFORM1 , INLINEFORM2 MATH INLINEFORM3 , INLINEFORM4 TIMEREF INLINEFORM5 respectively  We also truncate intervened threads to only include posts before the first instructor post since the instructor's and subsequent posts will bias the prediction due to the instructor's post 
Model
The key innovation of our work is to decompose the intervention prediction problem into a two-stage model that first explicitly tries to discover the proper context to which a potential intervention could be replying to, and then, predict the intervention status  This model implicitly assesses the importance (or urgency) of the existing thread's context to decide whether an intervention is necessary  For example in Figure SECREF1 , prior to the instructor's intervention, the ultimate post (Post #6) by Student 2 already acknowledged the OP's gratitude for his answer  In this regard, the instructor may have decided to use this point to summarize the entire thread to consolidate all the pertinent positions  Here, we might assume that the instructor's reply takes the entire thread (Posts #1–6) as the context for her reply 
This subproblem of inferring the context scope is where our innovation centers on  To be clear, in order to make the prediction that a instruction intervention is now necessary on a thread, the instructor's reply is not yet available — the model predicts whether a reply is necessary — so in the example, only Posts #1–6 are available in the problem setting  To infer the context, we have to decide which subsequence of posts are the most plausible motivation for an intervention 
Recent work in deep neural modeling has used an attention mechanism as a focusing query to highlight specific items within the input history that significantly influence the current decision point  Our work employs this mechanism – but with a twist: due to the fact that the actual instructor intervention is not (yet) available at the decision timing, we cannot use any actual intervention to decide the context  To employ attention, we must then employ a surrogate text as the query to train our prediction model  Our model variants model assess the suitability of such surrogate texts for the attention mechanism basis 
Congruent with the representation of the input forums, in all our proposed models, we encode the discussion thread hierarchically  We first build representations for each post by passing pre-trained word vector representations from GloVe BIBREF9 for each word through an LSTM BIBREF10 , INLINEFORM0   We use the last layer output of the LSTM as a representation of the post  We refer this as the post vector INLINEFORM1  
Then each post INLINEFORM0 is passed through another LSTM, INLINEFORM1 , whose last layer output forms the encoding of the entire thread  Hidden unit outputs of INLINEFORM2 represent the contexts INLINEFORM3 ; that is, snapshots of the threads after each post, as shown in Figure FIGREF1  
The INLINEFORM0 and INLINEFORM1 together constitute the hierarchical LSTM (hLSTM) model  This general hLSTM model serves as the basis for our model exploration in the rest of this section 
Contextual Attention Models
When they intervene, instructors either pay attention to a specific post or a series of posts, which trigger their reply  However, instructors rarely explicitly indicate to which post(s) their intervention is in relation to  This is the case in our corpus, party due to Coursera's user interface which only allows for single level comments (see Figure FIGREF2 )  Based solely on the binary, thread-level intervention signal, our secondary objective seeks to infer the appropriate context – represented by a sequence of posts – as the basis for the intervention 
We only consider linear contiguous series of posts starting with the thread's original post to constitute to a context; e g , INLINEFORM0   This is a reasonable as MOOC forum posts always reply to the original post or to a subsequent post, which in turn replies to the original post  This is in contrast to forums such as Reddit that have a tree or graph-like structure that require forum structure to be modelled explicitly, such as in BIBREF5  
We propose three neural attention BIBREF11 variants based on how an instructor might attend and reply to a context in a thread: the ultimate, penultimate and any post attention models  We review each of these in turn 
Ultimate Post Attention (UPA) Model  In this model we attend to the context represented by hidden state of the INLINEFORM0   We use the post prior to the instructor's reply as a query over the contexts INLINEFORM1 to compute attention weights INLINEFORM2 , which are then used to compute the attended context representation INLINEFORM3 (recall again that the intervention text itself is not available for this purpose)  This attention formulation makes an equivalence between the final INLINEFORM4 post and the prospective intervention, using Post INLINEFORM5 as the query for finding the appropriate context INLINEFORM6 , inclusive of itself INLINEFORM7   Said in another way, UPA uses the most recent content in the thread as the attentional query for context 
For example, if post INLINEFORM0 is the instructor's reply, post INLINEFORM1 will query over the contexts INLINEFORM2 and INLINEFORM3   The model schematic is shown in Figure FIGREF12  
The attended context representations are computed as: DISPLAYFORM0
The INLINEFORM0 representation is then passed through a fully connected softmax layer to yield the binary prediction 
Penultimate Post Attention (PPA) Model  While the UPA model uses the most recent text and makes the ultimate post itself available as potential context, our the ultimate post may be better modeled as having any of its prior posts as potential context  Penultimate Post Attention (PPA) variant does this  The schematic and the equations for the PPA model are obtained by summing over contexts INLINEFORM0 in Equation EQREF10 and Figure FIGREF12   While we could properly model such a context inference decision with any post INLINEFORM1 and prospective contexts INLINEFORM2 (where INLINEFORM3 is a random post), it makes sense to use the penultimate post, as we can make the most information available to the model for the context inference 
The attended context representations are computed as: DISPLAYFORM0
Any Post Attention (APA) Model  APA further relaxes both UPA and PPA, allowing APA to generalize and hypothesize that the prospective instructor intervention is based on the context that any previous post INLINEFORM0 replied to  In this model, each post INLINEFORM1 is set as a query to attend to its previous context INLINEFORM2   For example, INLINEFORM3 will attend to INLINEFORM4   Different from standard attention mechanisms, APA attention weights INLINEFORM5 are obtained by normalising interaction matrix over the different queries 
In APA, the attention context INLINEFORM0 is computed via: DISPLAYFORM0
Evaluation
The baseline and the models are evaluated on a corpus of 12 MOOC discussion forums  We train on 80% of the training data and report evaluation results on the held-out 20% of test data  We report INLINEFORM0 scores on the positive class (interventions), in line with prior work  We also argue that recall of the positive class is more important than precision, since it is costlier for instructors to miss intervening on a thread than spending irrelevant time intervening on a less critical threads due to false positives 
Model hyperpameter settings  All proposed and baseline neural models are trained using Adam optimizer with a learning rate of 0 001  We used cross-entropy as loss function  Importantly we updated the model parameters during training after each instance as in vanilla stochastic gradient descent; this setting was practical since data on most courses had only a few hundred instances enabling convergence within a reasonable training time of a few hours (see Table TABREF15 , column 2)  Models were trained for a single epoch as most of our courses with a few hundred thread converged after a single epoch  We used 300-dimensional GloVe vectors and permitted the embeddings to be updated during the model's end-to-end training  The hidden dimension size of both INLINEFORM0 and INLINEFORM1 are set to 128 for all the models 
Baselines  We compare our models against a neural baseline models, hierarchical LSTM (hLSTM), with the attention ablated but with access to the complete context, and a strong, open-sourced feature-rich baseline BIBREF7   We choose BIBREF7 over other prior works such as BIBREF0 since we do not have access to the dataset or the system used in their papers for replication  BIBREF7 is a logistic regression classifier with features inclusive of bag-of-words representation of the unigrams and thread length, normalised counts of agreements to previous posts, counts of non-lexical reference items such as URLs, and the Coursera forum type in which a thread appeared  We also report aggregated results from a hLSTM model with access only to the last post as context for comparison  Table TABREF17 compares the performance of these baselines against our proposed methods 
Results
Table TABREF15 shows performance of all our proposed models and the neural baseline over our 12 MOOC dataset  Our models of UPA, PPA individually better the baseline by 5 and 2% on INLINEFORM0 and 3 and 6% on recall respectively  UPA performs the best in terms of INLINEFORM1 on average while PPA performs the best in terms of recall on average  At the individual course level, however, the results are mixed  UPA performs the best on INLINEFORM2 on 5 out of 12 courses, PPA on 3 out 12 courses, APA 1 out of 12 courses and the baseline hLSTM on 1  PPA performs the best on recall on 7 out of the 12 courses  We also note that course level performance differences correlate with the course size and intervention ratio (hereafter, i ratio), which is the ratio of intervened to non-intervened threads  UPA performs better than PPA and APA on low intervention courses (i ratio INLINEFORM3 0 25) mainly because PPA and APA's performance drops steeply when i ratio drops (see col 2 parenthesis and INLINEFORM4 of PPA and APA)  While all the proposed models beat the baseline on every course except casebased-2  On medicalneuro-2 and compilers-4 which have the lowest i ratio among the 12 courses none of the neural models better the reported baseline BIBREF7 (course level not scores not shown in this paper)  The effect is pronounced in compilers-4 course where none of the neural models were able to predict any intervened threads  This is due to the inherent weakness of standard neural models, which are unable to learn features well enough when faced with sparse data 
The best performance of UPA indicates that the reply context of the instructor's post INLINEFORM0 correlates strongly with that of the previous post INLINEFORM1   This is not surprising since normal conversations are typically structured that way 
Discussion
In order to further understand the models' ability to infer the context and its effect on intervention prediction, we further investigate the following research questions 
RQ1  Does context inference help intervention prediction?
In order to understand if context inference is useful to intervention prediction, we ablate the attention components and experiment with the vanilla hierarchical LSTM model  Row 3 of Table TABREF17 shows the macro averaged result from this experiment  The UPA and PPA attention models better the vanilla hLSTM by 5% and 2% on average in INLINEFORM0 respectively  Recall that the vanilla hLSTM already has access to a context consisting of all posts (from INLINEFORM1 through INLINEFORM2 )  In contrast, the UPA and PPA models selectively infers a context for INLINEFORM3 and INLINEFORM4 posts, respectively, and use it to predict intervention  The improved performance of our attention models that actively select their optimal context, over a model with the complete thread as context, hLSTM, shows that the context inference improves intervention prediction over using the default full context 
RQ2  How well do the models perform across threads of different lengths? To understand the models' prediction performance across threads of different lengths, we bin threads by length and study the models' recall  We choose three courses, ml-5, rprog-3 and calc-1, from our corpus of 12 with the highest number of positive instances ( INLINEFORM0 100 threads)  We limit our analysis to these since binning renders courses with fewer positive instances sparse  Figure FIGREF18 shows performance across thread lengths from 1 through 7 posts and INLINEFORM1 posts  Clearly, the UPA model performs much better on shorter threads than on longer threads while PPA and APA works better on longer threads  Although, UPA is the best performing model in terms of overall INLINEFORM2 its performance drops steeply on threads of length INLINEFORM3   UPA's overall best performance is because most of the interventions in the corpus happen after one post  To highlight the performance of APA we show an example from smac-1 in Figure FIGREF22 with nine posts which was predicted correctly as intervened by APA but not by other models  Threads shows students confused over a missing figure in a homework  The instructor finally shows up, though late, to resolve the confusion 
RQ3  Do models trained with different context lengths perform better than when trained on a single context length?
We find that context length has a regularising effect on the model's performance at test time  This is not surprising since models trained with threads of single context length will not generalise to infer different context lengths  Row 4 of Table TABREF17 shows a steep performance drop in training by classifier with all threads truncated to a context of just one post, INLINEFORM0 , the post immediately preceding the intervened post  We also conducted an experiment with a multi-objective loss function with an additive cross-entropy term where each term computes loss from a model with context limited to a length of 3  We chose 3 since intervened threads in all the courses had a median length between 3 and 4  We achieved an INLINEFORM1 of 0 45 with a precision of 0 47 and recall of 0 43  This achieves a performance comparable to that of the BIBREF7 with context length set to only to 3  This approach of using infinitely many loss terms for each context length from 1 through the maximum thread length in a course is naive and not practical  We only use this model to show the importance of training the model with loss from threads of different lengths to prevent models overfitting to threads of specific context lengths 
Conclusion
We predict instructor intervention on student discussions by first inferring the optimal size of the context needed to decide on the intervention decision for the intervened post  We first show that a structured representation of the complete thread as the context is better than a bag-of-words, feature-rich representation  We then propose attention-based models to infer and select a context – defined as a contiguous subsequence of student posts – to improve over a model that always takes the complete thread as a context to prediction intervention  Our Any Post Attention (APA) model enables instructors to tune the model to predict intervention early or late  We posit our APA model will enable MOOC instructors employing varying pedagogical styles to use the model equally well  We introspect the attention models' performance across threads of varying lengths and show that APA predicts intervention on longer threads, which possesses more candidate contexts, better 
We note that the recall of the predictive models for longer threads (that is, threads of length greater 2) can still be improved  Models perform differently between shorter and longer length  An ensemble model or a multi-objective loss function is thus planned in our future work to better prediction on such longer threads 
Context 2: Introduction
Language modelling in its inception had one-hot vector encoding of words  However, it captures only alphabetic ordering but not the word semantic similarity  Vector space models helps to learn word representations in a lower dimensional space and also captures semantic similarity  Learning word embedding aids in natural language processing tasks such as question answering and reasoning BIBREF0, stance detection BIBREF1, claim verification BIBREF2 
Recent models BIBREF3, BIBREF4 work on the basis that words with similar context share semantic similarity  BIBREF4 proposes a neural probabilistic model which models the target word probability conditioned on the previous words using a recurrent neural network  Word2Vec models BIBREF3 such as continuous bag-of-words (CBOW) predict the target word given the context, and skip-gram model works in reverse of predicting the context given the target word  While, GloVe embeddings were based on a Global matrix factorization on local contexts BIBREF5  However, the aforementioned models do not handle words with multiple meanings (polysemies) 
BIBREF6 proposes a neural network approach considering both local and global contexts in learning word embeddings (point estimates)  Their multiple prototype model handles polysemous words by providing apriori heuristics about word senses in the dataset  BIBREF7 proposes an alternative to handle polysemous words by a modified skip-gram model and EM algorithm  BIBREF8 presents a non-parametric based alternative to handle polysemies  However, these approaches fail to consider entailment relations among the words  BIBREF9 learn a Gaussian distribution per word using the expected likelihood kernel  However, for polysemous words, this may lead to word distributions with larger variances as it may have to cover various senses 
BIBREF10 proposes multimodal word distribution approach  It captures polysemy  However, the energy based objective function fails to consider asymmetry and hence entailment  Textual entailment recognition is necessary to capture lexical inference relations such as causality (for example, mosquito $\rightarrow $ malaria), hypernymy (for example, dog $\models $ animal) etc 
In this paper, we propose to obtain multi-sense word embedding distributions by using a variant of max margin objective based on the asymmetric KL divergence energy function to capture textual entailment  Multi-sense distributions are advantageous in capturing polysemous nature of words and in reducing the uncertainty per word by distributing it across senses  However, computing KL divergence between mixtures of Gaussians is intractable, and we use a KL divergence approximation based on stricter upper and lower bounds  While capturing textual entailment (asymmetry), we have also not compromised on capturing symmetrical similarity between words (for example, funny and hilarious) which will be elucidated in Section $3 1$  We also show the effectiveness of the proposed approach on the benchmark word similarity and entailment datasets in the experimental section 
Methodology ::: Word Representation
Probabilistic representation of words helps one model uncertainty in word representation, and polysemy  Given a corpus $V$, containing a list of words each represented as $w$, the probability density for a word $w$ can be represented as a mixture of Gaussians with $C$ components BIBREF10 
Here, $p_{w,j}$ represents the probability of word $w$ belonging to the component $j$, $\operatorname{\mathbf {\mu }}_{w,j}$ represents $D$ dimensional word representation corresponding to the $j^{th}$ component sense of the word $w$, and $\Sigma _{w,j}$ represents the uncertainty in representation for word $w$ belonging to component $j$ 
Objective function
The model parameters (means, covariances and mixture weights) $\theta $ can be learnt using a variant of max-margin objective BIBREF11 
Here $E_\theta (\cdot , \cdot )$ represents an energy function which assigns a score to the pair of words, $w$ is a particular word under consideration, $c$ its positive context (same context), and $c^{\prime }$ the negative context  The objective aims to push the margin of the difference between the energy function of a word $w$ to its positive context $c$ higher than its negative context $c$ by a threshold of $m$  Thus, word pairs in the same context gets a higher energy than the word pairs in the dissimilar context  BIBREF10 consider the energy function to be an expected likelihood kernel which is defined as follows 
This is similar to the cosine similarity metric over vectors and the energy between two words is maximum when they have similar distributions  But, the expected likelihood kernel is a symmetric metric which will not be suitable for capturing ordering among words and hence entailment 
Objective function ::: Proposed Energy function
As each word is represented by a mixture of Gaussian distributions, KL divergence is a better choice of energy function to capture distance between distributions  Since, KL divergence is minimum when the distributions are similar and maximum when they are dissimilar, energy function is taken as exponentiated negative KL divergence 
However, computing KL divergence between Gaussian mixtures is intractable and obtaining exact KL value is not possible  One way of approximating the KL is by Monte-Carlo approximation but it requires large number of samples to get a good approximation and is computationally expensive on high dimensional embedding space 
Alternatively, BIBREF12 presents a KL approximation between Gaussian mixtures where they obtain an upper bound through product of Gaussian approximation method and a lower bound through variational approximation method  In BIBREF13, the authors combine the lower and upper bounds from approximation methods of BIBREF12 to provide a stricter bound on KL between Gaussian mixtures  Lets consider Gaussian mixtures for the words $w$ and $v$ as follows 
The approximate KL divergence between the Gaussian mixture representations over the words $w$ and $v$ is shown in equation DISPLAY_FORM8  More details on approximation is included in the Supplementary Material 
where $EL_{ik}(w,w) = \int f_{w,i} (\operatorname{\mathbf {x}}) f_{w,k} (\operatorname{\mathbf {x}}) d\operatorname{\mathbf {x}}$ and $EL_{ij}(w,v) = \int f_{w,i} (\operatorname{\mathbf {x}}) f_{v,k} (\operatorname{\mathbf {x}}) d\operatorname{\mathbf {x}}$  Note that the expected likelihood kernel appears component wise inside the approximate KL divergence derivation 
One advantage of using KL as energy function is that it enables to capture asymmetry in entailment datasets  For eg , let us consider the words 'chair' with two senses as 'bench' and 'sling', and 'wood' with two senses as 'trees' and 'furniture'  The word chair ($w$) is entailed within wood ($v$), i e  chair $\models $ wood  Now, minimizing the KL divergence necessitates maximizing $\log {\sum _j p_{v,j} \exp ({-KL(f_{w,i} (\operatorname{\mathbf {x}})||f_{v,j}(\operatorname{\mathbf {x}}))})}$ which in turn minimizes $KL(f_{w,i}(\operatorname{\mathbf {x}})||f_{v,j}(\operatorname{\mathbf {x}}))$  This will result in the support of the $i^{th}$ component of $w$ to be within the $j^{th}$ component of $v$, and holds for all component pairs leading to the entailment of $w$ within $v$  Consequently, we can see that bench $\models $ trees, bench $\models $ furniture, sling $\models $ trees, and sling $\models $ furniture  Thus, it introduces lexical relationship between the senses of child word and that of the parent word  Minimizing the KL also necessitates maximizing $\log {\sum _j {p_{v,j}} EL_{ij}(w,v)}$ term for all component pairs among $w$ and $v$  This is similar to maximizing expected likelihood kernel, which brings the means of $f_{w,i}(\operatorname{\mathbf {x}})$ and $f_{v,j}(\operatorname{\mathbf {x}})$ closer (weighted by their co-variances) as discussed in BIBREF10  Hence, the proposed approach captures the best of both worlds, thereby catering to both word similarity and entailment 
We also note that minimizing the KL divergence necessitates minimizing $\log {\sum _k p_{w,k} \exp ({-KL(f_{w,i}||f_{w,k})})}$ which in turn maximizes $KL(f_{w,i}||f_{w,k})$  This prevents the different mixture components of a word converging to single Gaussian and encourages capturing different possible senses of the word  The same is also achieved by minimizing $\sum _k {p_{w,k}} EL_{ik}(w,w)$ term and act as a regularization term which promotes diversity in learning senses of a word 
Experimentation and Results
We train our proposed model GM$\_$KL (Gaussian Mixture using KL Divergence) on the Text8 dataset BIBREF14 which is a pre-processed data of $17M$ words from wikipedia  Of which, 71290 unique and frequent words are chosen using the subsampling trick in BIBREF15  We compare GM$\_$KL with the previous approaches w2g BIBREF9 ( single Gaussian model) and w2gm BIBREF10 (mixture of Gaussian model with expected likelihood kernel)  For all the models used for experimentation, the embedding size ($D$) was set to 50, number of mixtures to 2, context window length to 10, batch size to 128  The word embeddings were initialized using a uniform distribution in the range of $[-\sqrt{\frac{3}{D}}$, $\sqrt{\frac{3}{D}}]$ such that the expectation of variance is 1 and mean 0 BIBREF16  One could also consider initializing the word embeddings using other contextual representations such as BERT BIBREF17 and ELMo BIBREF18 in the proposed approach  In order to purely analyze the performance of $\emph {GM\_KL}$ over the other models, we have chosen initialization using uniform distribution for experiments  For computational benefits, diagonal covariance is used similar to BIBREF10  Each mixture probability is constrained in the range $[0,1]$, summing to 1 by optimizing over unconstrained scores in the range $(-\infty ,\infty )$ and converting scores to probability using softmax function  The mixture scores are initialized to 0 to ensure fairness among all the components  The threshold for negative sampling was set to $10^{-5}$, as recommended in BIBREF3  Mini-batch gradient descent with Adagrad optimizer BIBREF19 was used with initial learning rate set to $0 05$ 
Table TABREF9 shows the qualitative results of GM$\_$KL  Given a query word and component id, the set of nearest neighbours along with their respective component ids are listed  For eg , the word `plane' in its 0th component captures the `geometry' sense and so are its neighbours, and its 1st component captures `vehicle' sense and so are its corresponding neighbours  Other words such as `rock' captures both the `metal' and `music' senses, `star' captures `celebrity' and `astronomical' senses, and `phone' captures `telephony' and `internet' senses 
We quantitatively compare the performance of the GM$\_$KL, w2g, and w2gm approaches on the SCWS dataset BIBREF6  The dataset consists of 2003 word pairs of polysemous and homonymous words with labels obtained by an average of 10 human scores  The Spearman correlation between the human scores and the model scores are computed  To obtain the model score, the following metrics are used:
MaxCos: Maximum cosine similarity among all component pairs of words $w$ and $v$:
AvgCos: Average component-wise cosine similarity between the words $w$ and $v$ 
KL$\_$approx: Formulated as shown in (DISPLAY_FORM8) between the words $w$ and $v$ 
KL$\_$comp: Maximum component-wise negative KL between words $w$ and $v$:
Table TABREF17 compares the performance of the approaches on the SCWS dataset  It is evident from Table TABREF17 that GM$\_$KL achieves better correlation than existing approaches for various metrics on SCWS dataset 
Table TABREF18 shows the Spearman correlation values of GM$\_$KL model evaluated on the benchmark word similarity datasets: SL BIBREF20, WS, WS-R, WS-S BIBREF21, MEN BIBREF22, MC BIBREF23, RG BIBREF24, YP BIBREF25, MTurk-287 and MTurk-771 BIBREF26, BIBREF27, and RW BIBREF28  The metric used for comparison is 'AvgCos'  It can be seen that for most of the datasets, GM$\_$KL achieves significantly better correlation score than w2g and w2gm approaches  Other datasets such as MC and RW consist of only a single sense, and hence w2g model performs better and GM$\_$KL achieves next better performance  The YP dataset have multiple senses but does not contain entailed data and hence could not make use of entailment benefits of GM$\_$KL 
Table TABREF19 shows the evaluation results of GM$\_$KL model on the entailment datasets such as entailment pairs dataset BIBREF29 created from WordNet with both positive and negative labels, a crowdsourced dataset BIBREF30 of 79 semantic relations labelled as entailed or not and annotated distributionally similar nouns dataset BIBREF31  The 'MaxCos' similarity metric is used for evaluation and the best precision and best F1-score is shown, by picking the optimal threshold  Overall, GM$\_$KL performs better than both w2g and w2gm approaches 
Conclusion
We proposed a KL divergence based energy function for learning multi-sense word embedding distributions modelled as Gaussian mixtures  Due to the intractability of the Gaussian mixtures for the KL divergence measure, we use an approximate KL divergence function  We also demonstrated that the proposed GM$\_$KL approaches performed better than other approaches on the benchmark word similarity and entailment datasets 
tocsectionAppendices
Approximation for KL divergence between mixtures of gaussians
KL between gaussian mixtures $f_{w}(\operatorname{\mathbf {x}})$ and $f_{v}(\operatorname{\mathbf {x}})$ can be decomposed as:
BIBREF12 presents KL approximation between gaussian mixtures using
product of gaussian approximation method where KL is approximated using product of component gaussians and
variational approximation method where KL is approximated by introducing some variational parameters 
The product of component gaussian approximation method using Jensen's inequality provides upper bounds as shown in equations DISPLAY_FORM23 and  
The variational approximation method provides lower bounds as shown in equations DISPLAY_FORM24 and DISPLAY_FORM25 
where $H$ represents the entropy term and the entropy of $i^{th}$ component of word $w$ with dimension $D$ is given as
In BIBREF13, the authors combine the lower and upper bounds from approximation methods of BIBREF12 to formulate a stricter bound on KL between gaussian mixtures 
From equations DISPLAY_FORM23 and DISPLAY_FORM25, a stricter lower bound for KL between gaussian mixtures is obtained as shown in equation DISPLAY_FORM26
From equations and DISPLAY_FORM24, a stricter upper bound for KL between gaussian mixtures is obtained as shown in equation DISPLAY_FORM27
Finally, the KL between gaussian mixtures is taken as the mean of KL upper and lower bounds as shown in equation DISPLAY_FORM28 
Context 3: Introduction
Chinese word segmentation (CWS) is a task for Chinese natural language process to delimit word boundary  CWS is a basic and essential task for Chinese which is written without explicit word delimiters and different from alphabetical languages like English  BIBREF0 treats Chinese word segmentation (CWS) as a sequence labeling task with character position tags, which is followed by BIBREF1, BIBREF2, BIBREF3  Traditional CWS models depend on the design of features heavily which effects the performance of model  To minimize the effort in feature engineering, some CWS models BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10, BIBREF11 are developed following neural network architecture for sequence labeling tasks BIBREF12  Neural CWS models perform strong ability of feature representation, employing unigram and bigram character embedding as input and approach good performance 
The CWS task is often modeled as one graph model based on a scoring model that means it is composed of two parts, one part is an encoder which is used to generate the representation of characters from the input sequence, the other part is a decoder which performs segmentation according to the encoder scoring  Table TABREF1 summarizes typical CWS models according to their decoding ways for both traditional and neural models  Markov models such as BIBREF13 and BIBREF4 depend on the maximum entropy model or maximum entropy Markov model both with a Viterbi decoder  Besides, conditional random field (CRF) or Semi-CRF for sequence labeling has been used for both traditional and neural models though with different representations BIBREF2, BIBREF15, BIBREF10, BIBREF17, BIBREF18  Generally speaking, the major difference between traditional and neural network models is about the way to represent input sentences 
Recent works about neural CWS which focus on benchmark dataset, namely SIGHAN Bakeoff BIBREF21, may be put into the following three categories roughly 
Encoder  Practice in various natural language processing tasks has been shown that effective representation is essential to the performance improvement  Thus for better CWS, it is crucial to encode the input character, word or sentence into effective representation  Table TABREF2 summarizes regular feature sets for typical CWS models including ours as well  The building blocks that encoders use include recurrent neural network (RNN) and convolutional neural network (CNN), and long-term memory network (LSTM) 
Graph model  As CWS is a kind of structure learning task, the graph model determines which type of decoder should be adopted for segmentation, also it may limit the capability of defining feature, as shown in Table 2, not all graph models can support the word features  Thus recent work focused on finding more general or flexible graph model to make model learn the representation of segmentation more effective as BIBREF9, BIBREF11 
External data and pre-trained embedding  Whereas both encoder and graph model are about exploring a way to get better performance only by improving the model strength itself  Using external resource such as pre-trained embeddings or language representation is an alternative for the same purpose BIBREF22, BIBREF23  SIGHAN Bakeoff defines two types of evaluation settings, closed test limits all the data for learning should not be beyond the given training set, while open test does not take this limitation BIBREF21  In this work, we will focus on the closed test setting by finding a better model design for further CWS performance improvement 
Shown in Table TABREF1, different decoders have particular decoding algorithms to match the respective CWS models  Markov models and CRF-based models often use Viterbi decoders with polynomial time complexity  In general graph model, search space may be too large for model to search  Thus it forces graph models to use an approximate beam search strategy  Beam search algorithm has a kind low-order polynomial time complexity  Especially, when beam width $b$=1, the beam search algorithm will reduce to greedy algorithm with a better time complexity $O(Mn)$ against the general beam search time complexity $O(Mnb^2)$, where $n$ is the number of units in one sentences, $M$ is a constant representing the model complexity  Greedy decoding algorithm can bring the fastest speed of decoding while it is not easy to guarantee the precision of decoding when the encoder is not strong enough 
In this paper, we focus on more effective encoder design which is capable of offering fast and accurate Chinese word segmentation with only unigram feature and greedy decoding  Our proposed encoder will only consist of attention mechanisms as building blocks but nothing else  Motivated by the Transformer BIBREF24 and its strength of capturing long-range dependencies of input sentences, we use a self-attention network to generate the representation of input which makes the model encode sentences at once without feeding input iteratively  Considering the weakness of the Transformer to model relative and absolute position information directly BIBREF25 and the importance of localness information, position information and directional information for CWS, we further improve the architecture of standard multi-head self-attention of the Transformer with a directional Gaussian mask and get a variant called Gaussian-masked directional multi-head attention  Based on the newly improved attention mechanism, we expand the encoder of the Transformer to capture different directional information  With our powerful encoder, our model uses only simple unigram features to generate representation of sentences 
For decoder which directly performs the segmentation, we use the bi-affinal attention scorer, which has been used in dependency parsing BIBREF26 and semantic role labeling BIBREF27, to implement greedy decoding on finding the boundaries of words  In our proposed model, greedy decoding ensures a fast segmentation while powerful encoder design ensures a good enough segmentation performance even working with greedy decoder together  Our model will be strictly evaluated on benchmark datasets from SIGHAN Bakeoff shared task on CWS in terms of closed test setting, and the experimental results show that our proposed model achieves new state-of-the-art 
The technical contributions of this paper can be summarized as follows 
We propose a CWS model with only attention structure  The encoder and decoder are both based on attention structure 
With a powerful enough encoder, we for the first time show that unigram (character) featues can help yield strong performance instead of diverse $n$-gram (character and word) features in most of previous work 
To capture the representation of localness information and directional information, we propose a variant of directional multi-head self-attention to further enhance the state-of-the-art Transformer encoder 
Models
The CWS task is often modelled as one graph model based on an encoder-based scoring model  The model for CWS task is composed of an encoder to represent the input and a decoder based on the encoder to perform actual segmentation  Figure FIGREF6 is the architecture of our model  The model feeds sentence into encoder  Embedding captures the vector $e=(e_1,   ,e_n)$ of the input character sequences of $c=(c_1,   ,c_n)$  The encoder maps vector sequences of $ {e}=(e_1,  ,e_n)$ to two sequences of vector which are $ {v^b}=(v_1^b,   ,v_n^b)$ and ${v^f}=(v_1^f,   v_n^f)$ as the representation of sentences  With $v^b$ and $v^f$, the bi-affinal scorer calculates the probability of each segmentation gaps and predicts the word boundaries of input

ground_truth: hLSTM

hLSTM

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 does this approach compare to other WSD approaches employing word embeddings?
Context 3:  Similar as the Transformer, the encoder is an attention network with stacked self-attention and point-wise, fully connected layers while our encoder includes three independent directional encoders 
Models ::: Encoder Stacks
In the Transformer, the encoder is composed of a stack of N identical layers and each layer has one multi-head self-attention layer and one position-wise fully connected feed-forward layer  One residual connection is around two sub-layers and followed by layer normalization BIBREF24  This architecture provides the Transformer a good ability to generate representation of sentence 
With the variant of multi-head self-attention, we design a Gaussian-masked directional encoder to capture representation of different directions to improve the ability of capturing the localness information and position information for the importance of adjacent characters  One unidirectional encoder can capture information of one particular direction 
For CWS tasks, one gap of characters, which is from a word boundary, can divide one sequence into two parts, one part in front of the gap and one part in the rear of it  The forward encoder and backward encoder are used to capture information of two directions which correspond to two parts divided by the gap 
One central encoder is paralleled with forward and backward encoders to capture the information of entire sentences  The central encoder is a special directional encoder for forward and backward information of sentences  The central encoder can fuse the information and enable the encoder to capture the global information 
The encoder outputs one forward information and one backward information of each positions  The representation of sentence generated by center encoder will be added to these information directly:
where $v^{b}=(v^b_1,   ,v^b_n)$ is the backward information, $v^{f}=(v^f_1,   ,v^f_n)$ is the forward information, $r^{b}=(r^b_1,   ,r^b_n)$ is the output of backward encoder, $r^{c}=(r^c_1,   ,r^c_n)$ is the output of center encoder and $r^{f}=(r^f_1,   ,r^f_n)$ is the output of forward encoder 
Models ::: Gaussian-Masked Directional Multi-Head Attention
Similar as scaled dot-product attention BIBREF24, Gaussian-masked directional attention can be described as a function to map queries and key-value pairs to the representation of input  Here queries, keys and values are all vectors  Standard scaled dot-product attention is calculated by dotting query $Q$ with all keys $K$, dividing each values by $\sqrt{d_k}$, where $\sqrt{d_k}$ is the dimension of keys, and apply a softmax function to generate the weights in the attention:
Different from scaled dot-product attention, Gaussian-masked directional attention expects to pay attention to the adjacent characters of each positions and cast the localness relationship between characters as a fix Gaussian weight for attention  We assume that the Gaussian weight only relys on the distance between characters 
Firstly we introduce the Gaussian weight matrix $G$ which presents the localness relationship between each two characters:
where $g_{ij}$ is the Gaussian weight between character $i$ and $j$, $dis_{ij}$ is the distance between character $i$ and $j$, $\Phi (x)$ is the cumulative distribution function of Gaussian, $\sigma $ is the standard deviation of Gaussian function and it is a hyperparameter in our method  Equation (DISPLAY_FORM13) can ensure the Gaussian weight equals 1 when $dis_{ij}$ is 0  The larger distance between charactersis, the smaller the weight is, which makes one character can affect its adjacent characters more compared with other characters 
To combine the Gaussian weight to the self-attention, we produce the Hadamard product of Gaussian weight matrix $G$ and the score matrix produced by $Q{K^{T}}$
where $AG$ is the Gaussian-masked attention  It ensures that the relationship between two characters with long distances is weaker than adjacent characters 
The scaled dot-product attention models the relationship between two characters without regard to their distances in one sequence  For CWS task, the weight between adjacent characters should be more important while it is hard for self-attention to achieve the effect explicitly because the self-attention cannot get the order of sentences directly  The Gaussian-masked attention adjusts the weight between characters and their adjacent character to a larger value which stands for the effect of adjacent characters 
For forward and backward encoder, the self-attention sublayer needs to use a triangular matrix mask to let the self-attention focus on different weights:
where $pos_i$ is the position of character $c_i$  The triangular matrix for forward and backward encode are:
$\left[ \begin{matrix} 1 & 0 & 0 & \cdots &0\\ 1 & 1 & 0 & \cdots &0\\ 1 & 1 & 1 & \cdots &0\\ \vdots &\vdots &\vdots &\ddots &\vdots \\ 1 & 1 & 1 & \cdots & 1\\ \end{matrix} \right]$ $\left[ \begin{matrix} 1 & 1 & 1 & \cdots &1 \\ 0 & 1 & 1 & \cdots &1 \\ 0 & 0& 1 & \cdots &1 \\ \vdots &\vdots &\vdots &\ddots &\vdots \\ 0 & 0 & 0 & \cdots & 1\\ \end{matrix}\right]$
Similar as BIBREF24, we use multi-head attention to capture information from different dimension positions as Figure FIGREF16 and get Gaussian-masked directional multi-head attention  With multi-head attention architecture, the representation of input can be captured by
where $MH$ is the Gaussian-masked multi-head attention, ${W_i^q, W_i^k,W_i^v} \in \mathbb {R}^{d_k \times d_h}$ is the parameter matrices to generate heads, $d_k$ is the dimension of model and $d_h$ is the dimension of one head 
Models ::: Bi-affinal Attention Scorer
Regarding word boundaries as gaps between any adjacent words converts the character labeling task to the gap labeling task  Different from character labeling task, gap labeling task requires information of two adjacent characters  The relationship between adjacent characters can be represented as the type of gap  The characteristic of word boundaries makes bi-affine attention an appropriate scorer for CWS task 
Bi-affinal attention scorer is the component that we use to label the gap  Bi-affinal attention is developed from bilinear attention which has been used in dependency parsing BIBREF26 and SRL BIBREF27  The distribution of labels in a labeling task is often uneven which makes the output layer often include a fixed bias term for the prior probability of different labels BIBREF27  Bi-affine attention uses bias terms to alleviate the burden of the fixed bias term and get the prior probability which makes it different from bilinear attention  The distribution of the gap is uneven that is similar as other labeling task which fits bi-affine 
Bi-affinal attention scorer labels the target depending on information of independent unit and the joint information of two units  In bi-affinal attention, the score $s_{ij}$ of characters $c_i$ and $c_j$ $(i < j)$ is calculated by:
where $v_i^f$ is the forward information of $c_i$ and $v_i^b$ is the backward information of $c_j$  In Equation (DISPLAY_FORM21), $W$, $U$ and $b$ are all parameters that can be updated in training  $W$ is a matrix with shape $(d_i \times N\times d_j)$ and $U$ is a $(N\times (d_i + d_j))$ matrix where $d_i$ is the dimension of vector $v_i^f$ and $N$ is the number of labels 
In our model, the biaffine scorer uses the forward information of character in front of the gap and the backward information of the character behind the gap to distinguish the position of characters  Figure FIGREF22 is an example of labeling gap  The method of using biaffine scorer ensures that the boundaries of words can be determined by adjacent characters with different directional information  The score vector of the gap is formed by the probability of being a boundary of word  Further, the model generates all boundaries using activation function in a greedy decoding way 
Experiments ::: Experimental Settings ::: Data
We train and evaluate our model on datasets from SIGHAN Bakeoff 2005 BIBREF21 which has four datasets, PKU, MSR, AS and CITYU  Table TABREF23 shows the statistics of train data  We use F-score to evaluate CWS models  To train model with pre-trained embeddings in AS and CITYU, we use OpenCC to transfer data from traditional Chinese to simplified Chinese 
Experiments ::: Experimental Settings ::: Pre-trained Embedding
We only use unigram feature so we only trained character embeddings  Our pre-trained embedding are pre-trained on Chinese Wikipedia corpus by word2vec BIBREF29 toolkit

ground_truth: GM$\_$KL achieves better correlation than existing approaches for various metrics on SCWS dataset.

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 are strong baselines model is compared to?
Context 3:  The corpus used for pre-trained embedding is all transferred to simplified Chinese and not segmented  On closed test, we use embeddings initialized randomly 
Experiments ::: Experimental Settings ::: Hyperparameters
For different datasets, we use two kinds of hyperparameters which are presented in Table TABREF24  We use hyperparameters in Table TABREF24 for small corpora (PKU and CITYU) and normal corpora (MSR and AS)  We set the standard deviation of Gaussian function in Equation (DISPLAY_FORM13) to 2  Each training batch contains sentences with at most 4096 tokens 
Experiments ::: Experimental Settings ::: Optimizer
To train our model, we use the Adam BIBREF30 optimizer with $\beta _1=0 9$, $\beta _2=0 98$ and $\epsilon =10^{-9}$  The learning rate schedule is the same as BIBREF24:
where $d$ is the dimension of embeddings, $step$ is the step number of training and $warmup_step$ is the step number of warmup  When the number of steps is smaller than the step of warmup, the learning rate increases linearly and then decreases 
Experiments ::: Hardware and Implements
We trained our models on a single CPU (Intel i7-5960X) with an nVidia 1080 Ti GPU  We implement our model in Python with Pytorch 1 0 
Experiments ::: Results
Tables TABREF25 and TABREF26 reports the performance of recent models and ours in terms of closed test setting  Without the assistance of unsupervised segmentation features userd in BIBREF20, our model outperforms all the other models in MSR and AS except BIBREF18 and get comparable performance in PKU and CITYU  Note that all the other models for this comparison adopt various $n$-gram features while only our model takes unigram ones 
With unsupervised segmentation features introduced by BIBREF20, our model gets a higher result  Specially, the results in MSR and AS achieve new state-of-the-art and approaching previous state-of-the-art in CITYU and PKU  The unsupervised segmentation features are derived from the given training dataset, thus using them does not violate the rule of closed test of SIGHAN Bakeoff 
Table TABREF36 compares our model and recent neural models in terms of open test setting in which any external resources, especially pre-trained embeddings or language models can be used  In MSR and AS, our model gets a comparable result while our results in CITYU and PKU are not remarkable 
However, it is well known that it is always hard to compare models when using open test setting, especially with pre-trained embedding  Not all models may use the same method and data to pre-train  Though pre-trained embedding or language model can improve the performance, the performance improvement itself may be from multiple sources  It often that there is a success of pre-trained embedding to improve the performance, while it cannot prove that the model is better 
Compared with other LSTM models, our model performs better in AS and MSR than in CITYU and PKU  Considering the scale of different corpora, we believe that the size of corpus affects our model and the larger size is, the better model performs  For small corpus, the model tends to be overfitting 
Tables TABREF25 and TABREF26 also show the decoding time in different datasets  Our model finishes the segmentation with the least decoding time in all four datasets, thanks to the architecture of model which only takes attention mechanism as basic block 
Related Work ::: Chinese Word Segmentation
CWS is a task for Chinese natural language process to delimit word boundary  BIBREF0 for the first time formulize CWS as a sequence labeling task  BIBREF3 show that different character tag sets can make essential impact for CWS  BIBREF2 use CRFs as a model for CWS, achieving new state-of-the-art  Works of statistical CWS has built the basis for neural CWS 
Neural word segmentation has been widely used to minimize the efforts in feature engineering which was important in statistical CWS  BIBREF4 introduce the neural model with sliding-window based sequence labeling  BIBREF6 propose a gated recursive neural network (GRNN) for CWS to incorporate complicated combination of contextual character and n-gram features  BIBREF7 use LSTM to learn long distance information  BIBREF9 propose a neural framework that eliminates context windows and utilize complete segmentation history  BIBREF33 explore a joint model that performs segmentation, POS-Tagging and chunking simultaneously  BIBREF34 propose a feature-enriched neural model for joint CWS and part-of-speech tagging  BIBREF35 present a joint model to enhance the segmentation of Chinese microtext by performing CWS and informal word detection simultaneously  BIBREF17 propose a character-based convolutional neural model to capture $n$-gram features automatically and an effective approach to incorporate word embeddings  BIBREF11 improve the model in BIBREF9 and propose a greedy neural word segmenter with balanced word and character embedding inputs  BIBREF23 propose a novel neural network model to incorporate unlabeled and partially-labeled data  BIBREF36 propose two methods that extend the Bi-LSTM to perform incorporating dictionaries into neural networks for CWS  BIBREF37 propose Switch-LSTMs to segment words and provided a more flexible solution for multi-criteria CWS which is easy to transfer the learned knowledge to new criteria 
Related Work ::: Transformer
Transformer BIBREF24 is an attention-based neural machine translation model  The Transformer is one kind of self-attention networks (SANs) which is proposed in BIBREF38  Encoder of the Transformer consists of one self-attention layer and a position-wise feed-forward layer  Decoder of the Transformer contains one self-attention layer, one encoder-decoder attention layer and one position-wise feed-forward layer  The Transformer uses residual connections around the sublayers and then followed by a layer normalization layer 
Scaled dot-product attention is the key component in the Transformer  The input of attention contains queries, keys, and values of input sequences  The attention is generated using queries and keys like Equation (DISPLAY_FORM11)  Structure of scaled dot-product attention allows the self-attention layer generate the representation of sentences at once and contain the information of the sentence which is different from RNN that process characters of sentences one by one  Standard self-attention is similar as Gaussian-masked direction attention while it does not have directional mask and gaussian mask  BIBREF24 also propose multi-head attention which is better to generate representation of sentence by dividing queries, keys and values to different heads and get information from different subspaces 
Conclusion
In this paper, we propose an attention mechanism only based Chinese word segmentation model  Our model uses self-attention from the Transformer encoder to take sequence input and bi-affine attention scorer to predict the label of gaps  To improve the ability of capturing the localness and directional information of self-attention based encoder, we propose a variant of self-attention called Gaussian-masked directional multi-head attention to replace the standard self-attention  We also extend the Transformer encoder to capture directional features  Our model uses only unigram features instead of multiple $n$-gram features in previous work  Our model is evaluated on standard benchmark dataset, SIGHAN Bakeoff 2005, which shows not only our model performs segmentation faster than any previous models but also gives new higher or comparable segmentation performance against previous state-of-the-art models 

ground_truth: Baseline models are:
- Chen et al., 2015a
- Chen et al., 2015b
- Liu et al., 2016
- Cai and Zhao, 2016
- Cai et al., 2017
- Zhou et al., 2017
- Ma et al., 2018
- Wang et al., 2019