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 is the dataset of hashtags sourced?
Context 1: Introduction
A hashtag is a keyphrase represented as a sequence of alphanumeric characters plus underscore, preceded by the # symbol  Hashtags play a central role in online communication by providing a tool to categorize the millions of posts generated daily on Twitter, Instagram, etc  They are useful in search, tracking content about a certain topic BIBREF0 , BIBREF1 , or discovering emerging trends BIBREF2  
Hashtags often carry very important information, such as emotion BIBREF3 , sentiment BIBREF4 , sarcasm BIBREF5 , and named entities BIBREF6 , BIBREF7   However, inferring the semantics of hashtags is non-trivial since many hashtags contain multiple tokens joined together, which frequently leads to multiple potential interpretations (e g , lion head vs  lionhead)  Table TABREF3 shows several examples of single- and multi-token hashtags  While most hashtags represent a mix of standard tokens, named entities and event names are prevalent and pose challenges to both human and automatic comprehension, as these are more likely to be rare tokens  Hashtags also tend to be shorter to allow fast typing, to attract attention or to satisfy length limitations imposed by some social media platforms  Thus, they tend to contain a large number of abbreviations or non-standard spelling variations (e g , #iloveu4eva) BIBREF8 , BIBREF9 , which hinders their understanding 
The goal of our study is to build efficient methods for automatically splitting a hashtag into a meaningful word sequence  Our contributions are:
Our new dataset includes segmentation for 12,594 unique hashtags and their associated tweets annotated in a multi-step process for higher quality than the previous dataset of 1,108 hashtags BIBREF10   We frame the segmentation task as a pairwise ranking problem, given a set of candidate segmentations  We build several neural architectures using this problem formulation which use corpus-based, linguistic and thesaurus based features  We further propose a multi-task learning approach which jointly learns segment ranking and single- vs  multi-token hashtag classification  The latter leads to an error reduction of 24 6% over the current state-of-the-art  Finally, we demonstrate the utility of our method by using hashtag segmentation in the downstream task of sentiment analysis  Feeding the automatically segmented hashtags to a state-of-the-art sentiment analysis method on the SemEval 2017 benchmark dataset results in a 2 6% increase in the official metric for the task 
Background and Preliminaries
Current approaches for hashtag segmentation can be broadly divided into three categories: (a) gazeteer and rule based BIBREF11 , BIBREF12 , BIBREF13 , (b) word boundary detection BIBREF14 , BIBREF15 , and (c) ranking with language model and other features BIBREF16 , BIBREF10 , BIBREF0 , BIBREF17 , BIBREF18   Hashtag segmentation approaches draw upon work on compound splitting for languages such as German or Finnish BIBREF19 and word segmentation BIBREF20 for languages with no spaces between words such as Chinese BIBREF21 , BIBREF22   Similar to our work, BIBREF10 BansalBV15 extract an initial set of candidate segmentations using a sliding window, then rerank them using a linear regression model trained on lexical, bigram and other corpus-based features  The current state-of-the-art approach BIBREF14 , BIBREF15 uses maximum entropy and CRF models with a combination of language model and hand-crafted features to predict if each character in the hashtag is the beginning of a new word 
Generating Candidate Segmentations  Microsoft Word Breaker BIBREF16 is, among the existing methods, a strong baseline for hashtag segmentation, as reported in BIBREF14 and BIBREF10   It employs a beam search algorithm to extract INLINEFORM0 best segmentations as ranked by the n-gram language model probability: INLINEFORM1
where INLINEFORM0 is the word sequence of segmentation INLINEFORM1 and INLINEFORM2 is the window size  More sophisticated ranking strategies, such as Binomial and word length distribution based ranking, did not lead to a further improvement in performance BIBREF16   The original Word Breaker was designed for segmenting URLs using language models trained on web data  In this paper, we reimplemented and tailored this approach to segmenting hashtags by using a language model specifically trained on Twitter data (implementation details in § SECREF26 )  The performance of this method itself is competitive with state-of-the-art methods (evaluation results in § SECREF46 )  Our proposed pairwise ranking method will effectively take the top INLINEFORM3 segmentations generated by this baseline as candidates for reranking 
However, in prior work, the ranking scores of each segmentation were calculated independently, ignoring the relative order among the top INLINEFORM0 candidate segmentations  To address this limitation, we utilize a pairwise ranking strategy for the first time for this task and propose neural architectures to model this 
Multi-task Pairwise Neural Ranking
We propose a multi-task pairwise neural ranking approach to better incorporate and distinguish the relative order between the candidate segmentations of a given hashtag  Our model adapts to address single- and multi-token hashtags differently via a multi-task learning strategy without requiring additional annotations  In this section, we describe the task setup and three variants of pairwise neural ranking models (Figure FIGREF11 ) 
Segmentation as Pairwise Ranking
The goal of hashtag segmentation is to divide a given hashtag INLINEFORM0 into a sequence of meaningful words INLINEFORM1   For a hashtag of INLINEFORM2 characters, there are a total of INLINEFORM3 possible segmentations but only one, or occasionally two, of them ( INLINEFORM4 ) are considered correct (Table TABREF9 ) 
We transform this task into a pairwise ranking problem: given INLINEFORM0 candidate segmentations { INLINEFORM1 }, we rank them by comparing each with the rest in a pairwise manner  More specifically, we train a model to predict a real number INLINEFORM2 for any two candidate segmentations INLINEFORM3 and INLINEFORM4 of hashtag INLINEFORM5 , which indicates INLINEFORM6 is a better segmentation than INLINEFORM7 if positive, and vice versa  To quantify the quality of a segmentation in training, we define a gold scoring function INLINEFORM8 based on the similarities with the ground-truth segmentation INLINEFORM9 : INLINEFORM10
We use the Levenshtein distance (minimum number of single-character edits) in this paper, although it is possible to use other similarity measurements as alternatives  We use the top INLINEFORM0 segmentations generated by Microsoft Word Breaker (§ SECREF2 ) as initial candidates 
Pairwise Neural Ranking Model
For an input candidate segmentation pair INLINEFORM0 , we concatenate their feature vectors INLINEFORM1 and INLINEFORM2 , and feed them into a feedforward network which emits a comparison score INLINEFORM3   The feature vector INLINEFORM4 or INLINEFORM5 consists of language model probabilities using Good-Turing BIBREF23 and modified Kneser-Ney smoothing BIBREF24 , BIBREF25 , lexical and linguistic features (more details in § SECREF23 )  For training, we use all the possible pairs INLINEFORM6 of the INLINEFORM7 candidates as the input and their gold scores INLINEFORM8 as the target  The training objective is to minimize the Mean Squared Error (MSE): DISPLAYFORM0
where INLINEFORM0 is the number of training examples 
To aggregate the pairwise comparisons, we follow a greedy algorithm proposed by BIBREF26 cohen1998learning and used for preference ranking BIBREF27   For each segmentation INLINEFORM0 in the candidate set INLINEFORM1 , we calculate a single score INLINEFORM2 , and find the segmentation INLINEFORM3 corresponding to the highest score  We repeat the same procedure after removing INLINEFORM4 from INLINEFORM5 , and continue until INLINEFORM6 reduces to an empty set  Figure FIGREF11 (a) shows the architecture of this model 
Margin Ranking (MR) Loss
As an alternative to the pairwise ranker (§ SECREF15 ), we propose a pairwise model which learns from candidate pairs INLINEFORM0 but ranks each individual candidate directly rather than relatively  We define a new scoring function INLINEFORM1 which assigns a higher score to the better candidate, i e , INLINEFORM2 , if INLINEFORM3 is a better candidate than INLINEFORM4 and vice-versa  Instead of concatenating the features vectors INLINEFORM5 and INLINEFORM6 , we feed them separately into two identical feedforward networks with shared parameters  During testing, we use only one of the networks to rank the candidates based on the INLINEFORM7 scores  For training, we add a ranking layer on top of the networks to measure the violations in the ranking order and minimize the Margin Ranking Loss (MR): DISPLAYFORM0
where INLINEFORM0 is the number of training samples  The architecture of this model is presented in Figure FIGREF11 (b) 
Adaptive Multi-task Learning
Both models in § SECREF15 and § SECREF17 treat all the hashtags uniformly  However, different features address different types of hashtags  By design, the linguistic features capture named entities and multi-word hashtags that exhibit word shape patterns, such as camel case  The ngram probabilities with Good-Turing smoothing gravitate towards multi-word segmentations with known words, as its estimate for unseen ngrams depends on the fraction of ngrams seen once which can be very low BIBREF28   The modified Kneser-Ney smoothing is more likely to favor segmentations that contain rare words, and single-word segmentations in particular  Please refer to § SECREF46 for a more detailed quantitative and qualitative analysis 
To leverage this intuition, we introduce a binary classification task to help the model differentiate single-word from multi-word hashtags  The binary classifier takes hashtag features INLINEFORM0 as the input and outputs INLINEFORM1 , which represents the probability of INLINEFORM2 being a multi-word hashtag  INLINEFORM3 is used as an adaptive gating value in our multi-task learning setup  The gold labels for this task are obtained at no extra cost by simply verifying whether the ground-truth segmentation has multiple words  We train the pairwise segmentation ranker and the binary single- vs  multi-token hashtag classifier jointly, by minimizing INLINEFORM4 for the pairwise ranker and the Binary Cross Entropy Error ( INLINEFORM5 ) for the classifier: DISPLAYFORM0
where INLINEFORM0 is the adaptive gating value, INLINEFORM1 indicates if INLINEFORM2 is actually a multi-word hashtag and INLINEFORM3 is the number of training examples  INLINEFORM4 and INLINEFORM5 are the weights for each loss  For our experiments, we apply equal weights 
More specifically, we divide the segmentation feature vector INLINEFORM0 into two subsets: (a) INLINEFORM1 with modified Kneser-Ney smoothing features, and (b) INLINEFORM2 with Good-Turing smoothing and linguistic features  For an input candidate segmentation pair INLINEFORM3 , we construct two pairwise vectors INLINEFORM4 and INLINEFORM5 by concatenation, then combine them based on the adaptive gating value INLINEFORM6 before feeding them into the feedforward network INLINEFORM7 for pairwise ranking: DISPLAYFORM0
We use summation with padding, as we find this simple ensemble method achieves similar performance in our experiments as the more complex multi-column networks BIBREF29   Figure FIGREF11 (c) shows the architecture of this model  An analogue multi-task formulation can also be used for the Margin Ranking loss as: DISPLAYFORM0
Features
We use a combination of corpus-based and linguistic features to rank the segmentations  For a candidate segmentation INLINEFORM0 , its feature vector INLINEFORM1 includes the number of words in the candidate, the length of each word, the proportion of words in an English dictionary or Urban Dictionary BIBREF30 , ngram counts from Google Web 1TB corpus BIBREF31 , and ngram probabilities from trigram language models trained on the Gigaword corpus BIBREF32 and 1 1 billion English tweets from 2010, respectively  We train two language models on each corpus: one with Good-Turing smoothing using SRILM BIBREF33 and the other with modified Kneser-Ney smoothing using KenLM BIBREF34   We also add boolean features, such as if the candidate is a named-entity present in the list of Wikipedia titles, and if the candidate segmentation INLINEFORM2 and its corresponding hashtag INLINEFORM3 satisfy certain word-shapes (more details in appendix SECREF61 ) 
Similarly, for hashtag INLINEFORM0 , we extract the feature vector INLINEFORM1 consisting of hashtag length, ngram count of the hashtag in Google 1TB corpus BIBREF31 , and boolean features indicating if the hashtag is in an English dictionary or Urban Dictionary, is a named-entity, is in camel case, ends with a number, and has all the letters as consonants  We also include features of the best-ranked candidate by the Word Breaker model 
Implementation Details
We use the PyTorch framework to implement our multi-task pairwise ranking model  The pairwise ranker consists of an input layer, three hidden layers with eight nodes in each layer and hyperbolic tangent ( INLINEFORM0 ) activation, and a single linear output node  The auxiliary classifier consists of an input layer, one hidden layer with eight nodes and one output node with sigmoid activation  We use the Adam algorithm BIBREF35 for optimization and apply a dropout of 0 5 to prevent overfitting  We set the learning rate to 0 01 and 0 05 for the pairwise ranker and auxiliary classifier respectively  For each experiment, we report results obtained after 100 epochs 
For the baseline model used to extract the INLINEFORM0 initial candidates, we reimplementated the Word Breaker BIBREF16 as described in § SECREF2 and adapted it to use a language model trained on 1 1 billion tweets with Good-Turing smoothing using SRILM BIBREF33 to give a better performance in segmenting hashtags (§ SECREF46 )  For all our experiments, we set INLINEFORM1  
Hashtag Segmentation Data
We use two datasets for experiments (Table TABREF29 ): (a) STAN INLINEFORM0 , created by BIBREF10 BansalBV15, which consists of 1,108 unique English hashtags from 1,268 randomly selected tweets in the Stanford Sentiment Analysis Dataset BIBREF36 along with their crowdsourced segmentations and our additional corrections; and (b) STAN INLINEFORM1 , our new expert curated dataset, which includes all 12,594 unique English hashtags and their associated tweets from the same Stanford dataset 
Experiments
In this section, we present experimental results that compare our proposed method with the other state-of-the-art approaches on hashtag segmentation datasets  The next section will show experiments of applying hashtag segmentation to the popular task of sentiment analysis 
Existing Methods
We compare our pairwise neural ranker with the following baseline and state-of-the-art approaches:
The original hashtag as a single token;
A rule-based segmenter, which employs a set of word-shape rules with an English dictionary BIBREF13 ;
A Viterbi model which uses word frequencies from a book corpus BIBREF0 ;
The specially developed GATE Hashtag Tokenizer from the open source toolkit, which combines dictionaries and gazetteers in a Viterbi-like algorithm BIBREF11 ;
A maximum entropy classifier (MaxEnt) trained on the STAN INLINEFORM0 training dataset  It predicts whether a space should be inserted at each position in the hashtag and is the current state-of-the-art BIBREF14 ;
Our reimplementation of the Word Breaker algorithm which uses beam search and a Twitter ngram language model BIBREF16 ;
A pairwise linear ranker which we implemented for comparison purposes with the same features as our neural model, but using perceptron as the underlying classifier BIBREF38 and minimizing the hinge loss between INLINEFORM0 and a scoring function similar to INLINEFORM1   It is trained on the STAN INLINEFORM2 dataset 
Evaluation Metrics
We evaluate the performance by the top INLINEFORM0 ( INLINEFORM1 ) accuracy (A@1, A@2), average token-level F INLINEFORM2 score (F INLINEFORM3 @1), and mean reciprocal rank (MRR)  In particular, the accuracy and MRR are calculated at the segmentation-level, which means that an output segmentation is considered correct if and only if it fully matches the human segmentation  Average token-level F INLINEFORM4 score accounts for partially correct segmentation in the multi-token hashtag cases 
Results
Tables TABREF32 and TABREF33 show the results on the STAN INLINEFORM0 and STAN INLINEFORM1 datasets, respectively  All of our pairwise neural rankers are trained on the 2,518 manually segmented hashtags in the training set of STAN INLINEFORM2 and perform favorably against other state-of-the-art approaches  Our best model (MSE+multitask) that utilizes different features adaptively via a multi-task learning procedure is shown to perform better than simply combining all the features together (MR and MSE)  We highlight the 24 6% error reduction on STAN INLINEFORM3 and 16 5% on STAN INLINEFORM4 of our approach over the previous SOTA BIBREF14 on the Multi-token hashtags, and the importance of having a separate evaluation of multi-word cases as it is trivial to obtain 100% accuracy for Single-token hashtags  While our hashtag segmentation model is achieving a very high accuracy@2, to be practically useful, it remains a challenge to get the top one predication exactly correct  Some hashtags are very difficult to interpret, e g , #BTVbrownSMB refers to the Social Media Breakfast (SMB) in Burlington, Vermont (BTV) 
The improved Word Breaker with our addition of a Twitter-specific language model is a very strong baseline, which echos the findings of the original Word Breaker paper BIBREF16 that having a large in-domain language model is extremely helpful for word segmentation tasks  It is worth noting that the other state-of-the-art system BIBREF14 also utilized a 4-gram language model trained on 476 million tweets from 2009 
Analysis and Discussion
To empirically illustrate the effectiveness of different features on different types of hashtags, we show the results for models using individual feature sets in pairwise ranking models (MSE) in Table TABREF45   Language models with modified Kneser-Ney smoothing perform best on single-token hashtags, while Good-Turing and Linguistic features work best on multi-token hashtags, confirming our intuition about their usefulness in a multi-task learning approach  Table TABREF47 shows a qualitative analysis with the first column ( INLINEFORM0 INLINEFORM1 INLINEFORM2 ) indicating which features lead to correct or wrong segmentations, their count in our data and illustrative examples with human segmentation 
As expected, longer hashtags with more than three tokens pose greater challenges and the segmentation-level accuracy of our best model (MSE+multitask) drops to 82 1%  For many error cases, our model predicts a close-to-correct segmentation, e g , #youbrownknowyoubrownupttoobrownearly, #iseebrownlondoniseebrownfrance, which is also reflected by the higher token-level F INLINEFORM0 scores across hashtags with different lengths (Figure FIGREF51 ) 
Since our approach heavily relies on building a Twitter language model, we experimented with its sizes and show the results in Figure FIGREF52   Our approach can perform well even with access to a smaller amount of tweets  The drop in F INLINEFORM0 score for our pairwise neural ranker is only 1 4% and 3 9% when using the language models trained on 10% and 1% of the total 1 1 billion tweets, respectively 
Language use in Twitter changes with time BIBREF9   Our pairwise ranker uses language models trained on the tweets from the year 2010  We tested our approach on a set of 500 random English hashtags posted in tweets from the year 2019 and show the results in Table TABREF55   With a segmentation-level accuracy of 94 6% and average token-level F INLINEFORM0 score of 95 6%, our approach performs favorably on 2019 hashtags 
Extrinsic Evaluation: Twitter Sentiment Analysis
We attempt to demonstrate the effectiveness of our hashtag segmentation system by studying its impact on the task of sentiment analysis in Twitter BIBREF39 , BIBREF40 , BIBREF41   We use our best model (MSE+multitask), under the name HashtagMaster, in the following experiments 
Experimental Setup
We compare the performance of the BiLSTM+Lex BIBREF42 sentiment analysis model under three configurations: (a) tweets with hashtags removed, (b) tweets with hashtags as single tokens excluding the # symbol, and (c) tweets with hashtags as segmented by our system, HashtagMaster  BiLSTM+Lex is a state-of-the-art open source system for predicting tweet-level sentiment BIBREF43   It learns a context-sensitive sentiment intensity score by leveraging a Twitter-based sentiment lexicon BIBREF44   We use the same settings as described by BIBREF42 teng-vo-zhang:2016:EMNLP2016 to train the model 
We use the dataset from the Sentiment Analysis in Twitter shared task (subtask A) at SemEval 2017 BIBREF41   Given a tweet, the goal is to predict whether it expresses POSITIVE, NEGATIVE or NEUTRAL sentiment  The training and development sets consist of 49,669 tweets and we use 40,000 for training and the rest for development  There are a total of 12,284 tweets containing 12,128 hashtags in the SemEval 2017 test set, and our hashtag segmenter ended up splitting 6,975 of those hashtags present in 3,384 tweets 
Results and Analysis
In Table TABREF59 , we report the results based on the 3,384 tweets where HashtagMaster predicted a split, as for the rest of tweets in the test set, the hashtag segmenter would neither improve nor worsen the sentiment prediction  Our hashtag segmenter successfully improved the sentiment analysis performance by 2% on average recall and F INLINEFORM0 comparing to having hashtags unsegmented  This improvement is seemingly small but decidedly important for tweets where sentiment-related information is embedded in multi-word hashtags and sentiment prediction would be incorrect based only on the text (see Table TABREF60 for examples)  In fact, 2,605 out of the 3,384 tweets have multi-word hashtags that contain words in the Twitter-based sentiment lexicon BIBREF44 and 125 tweets contain sentiment words only in the hashtags but not in the rest of the tweet  On the entire test set of 12,284 tweets, the increase in the average recall is 0 5% 
Other Related Work
Automatic hashtag segmentation can improve the performance of many applications besides sentiment analysis, such as text classification BIBREF13 , named entity linking BIBREF10 and modeling user interests for recommendations BIBREF45   It can also help in collecting data of higher volume and quality by providing a more nuanced interpretation of its content, as shown for emotion analysis BIBREF46 , sarcasm and irony detection BIBREF11 , BIBREF47   Better semantic analysis of hashtags can also potentially be applied to hashtag annotation BIBREF48 , to improve distant supervision labels in training classifiers for tasks such as sarcasm BIBREF5 , sentiment BIBREF4 , emotions BIBREF3 ; and, more generally, as labels for pre-training representations of words BIBREF49 , sentences BIBREF50 , and images BIBREF51  
Conclusion
We proposed a new pairwise neural ranking model for hashtag segmention and showed significant performance improvements over the state-of-the-art  We also constructed a larger and more curated dataset for analyzing and benchmarking hashtag segmentation methods  We demonstrated that hashtag segmentation helps with downstream tasks such as sentiment analysis  Although we focused on English hashtags, our pairwise ranking approach is language-independent and we intend to extend our toolkit to languages other than English as future work 
Acknowledgments
We thank Ohio Supercomputer Center BIBREF52 for computing resources and the NVIDIA for providing GPU hardware  We thank Alan Ritter, Quanze Chen, Wang Ling, Pravar Mahajan, and Dushyanta Dhyani for valuable discussions  We also thank the annotators: Sarah Flanagan, Kaushik Mani, and Aswathnarayan Radhakrishnan  This material is based in part on research sponsored by the NSF under grants IIS-1822754 and IIS-1755898, DARPA through the ARO under agreement number W911NF-17-C-0095, through a Figure-Eight (CrowdFlower) AI for Everyone Award and a Criteo Faculty Research Award to Wei Xu  The views and conclusions contained in this publication are those of the authors and should not be interpreted as representing official policies or endorsements of the U S  Government 
Word-shape rules
Our model uses the following word shape rules as boolean features  If the candidate segmentation INLINEFORM0 and its corresponding hashtag INLINEFORM1 satisfies a word shape rule, then the boolean feature is set to True 
Context 2: Introduction
The task of generating natural language descriptions of structured data (such as tables) BIBREF2 , BIBREF3 , BIBREF4 has seen a growth in interest with the rise of sequence to sequence models that provide an easy way of encoding tables and generating text from them BIBREF0 , BIBREF1 , BIBREF5 , BIBREF6  
For text generation tasks, the only gold standard metric is to show the output to humans for judging its quality, but this is too expensive to apply repeatedly anytime small modifications are made to a system  Hence, automatic metrics that compare the generated text to one or more reference texts are routinely used to compare models BIBREF7   For table-to-text generation, automatic evaluation has largely relied on BLEU BIBREF8 and ROUGE BIBREF9   The underlying assumption behind these metrics is that the reference text is gold-standard, i e , it is the ideal target text that a system should generate  In practice, however, when datasets are collected automatically and heuristically, the reference texts are often not ideal  Figure FIGREF2 shows an example from the WikiBio dataset BIBREF0   Here the reference contains extra information which no system can be expected to produce given only the associated table  We call such reference texts divergent from the table 
We show that existing automatic metrics, including BLEU, correlate poorly with human judgments when the evaluation sets contain divergent references (§ SECREF36 )  For many table-to-text generation tasks, the tables themselves are in a pseudo-natural language format (e g , WikiBio, WebNLG BIBREF6 , and E2E-NLG BIBREF10 )  In such cases we propose to compare the generated text to the underlying table as well to improve evaluation  We develop a new metric, PARENT (Precision And Recall of Entailed N-grams from the Table) (§ SECREF3 )  When computing precision, PARENT effectively uses a union of the reference and the table, to reward correct information missing from the reference  When computing recall, it uses an intersection of the reference and the table, to ignore extra incorrect information in the reference  The union and intersection are computed with the help of an entailment model to decide if a text n-gram is entailed by the table  We show that this method is more effective than using the table as an additional reference  Our main contributions are:
Table-to-Text Generation
We briefly review the task of generating natural language descriptions of semi-structured data, which we refer to as tables henceforth BIBREF11 , BIBREF12   Tables can be expressed as set of records INLINEFORM0 , where each record is a tuple (entity, attribute, value)  When all the records are about the same entity, we can truncate the records to (attribute, value) pairs  For example, for the table in Figure FIGREF2 , the records are {(Birth Name, Michael Dahlquist), (Born, December 22 1965),    }  The task is to generate a text INLINEFORM1 which summarizes the records in a fluent and grammatical manner  For training and evaluation we further assume that we have a reference description INLINEFORM2 available for each table  We let INLINEFORM3 denote an evaluation set of tables, references and texts generated from a model INLINEFORM4 , and INLINEFORM5 , INLINEFORM6 denote the collection of n-grams of order INLINEFORM7 in INLINEFORM8 and INLINEFORM9 , respectively  We use INLINEFORM10 to denote the count of n-gram INLINEFORM11 in INLINEFORM12 , and INLINEFORM13 to denote the minimum of its counts in INLINEFORM14 and INLINEFORM15   Our goal is to assign a score to the model, which correlates highly with human judgments of the quality of that model 
PARENT
PARENT evaluates each instance INLINEFORM0 separately, by computing the precision and recall of INLINEFORM1 against both INLINEFORM2 and INLINEFORM3  
Evaluation via Information Extraction
BIBREF1 proposed to use an auxiliary model, trained to extract structured records from text, for evaluation  However, the extraction model presented in that work is limited to the closed-domain setting of basketball game tables and summaries  In particular, they assume that each table has exactly the same set of attributes for each entity, and that the entities can be identified in the text via string matching  These assumptions are not valid for the open-domain WikiBio dataset, and hence we train our own extraction model to replicate their evaluation scheme 
Our extraction system is a pointer-generator network BIBREF19 , which learns to produce a linearized version of the table from the text  The network learns which attributes need to be populated in the output table, along with their values  It is trained on the training set of WikiBio  At test time we parsed the output strings into a set of (attribute, value) tuples and compare it to the ground truth table  The F-score of this text-to-table system was INLINEFORM0 , which is comparable to other challenging open-domain settings BIBREF20   More details are included in the Appendix SECREF52  
Given this information extraction system, we consider the following metrics for evaluation, along the lines of BIBREF1   Content Selection (CS): F-score for the (attribute, value) pairs extracted from the generated text compared to those extracted from the reference  Relation Generation (RG): Precision for the (attribute, value) pairs extracted from the generated text compared to those in the ground truth table  RG-F: Since our task emphasizes the recall of information from the table as well, we consider another variant which computes the F-score of the extracted pairs to those in the table  We omit the content ordering metric, since our extraction system does not align records to the input text 
Experiments & Results
In this section we compare several automatic evaluation metrics by checking their correlation with the scores assigned by humans to table-to-text models  Specifically, given INLINEFORM0 models INLINEFORM1 , and their outputs on an evaluation set, we show these generated texts to humans to judge their quality, and obtain aggregated human evaluation scores for all the models, INLINEFORM2 (§ SECREF33 )  Next, to evaluate an automatic metric, we compute the scores it assigns to each model, INLINEFORM3 , and check the Pearson correlation between INLINEFORM4 and INLINEFORM5 BIBREF21  
Data & Models
Our main experiments are on the WikiBio dataset BIBREF0 , which is automatically constructed and contains many divergent references  In § SECREF47 we also present results on the data released as part of the WebNLG challenge 
We developed several models of varying quality for generating text from the tables in WikiBio  This gives us a diverse set of outputs to evaluate the automatic metrics on  Table TABREF32 lists the models along with their hyperparameter settings and their scores from the human evaluation (§ SECREF33 )  Our focus is primarily on neural sequence-to-sequence methods since these are most widely used, but we also include a template-based baseline  All neural models were trained on the WikiBio training set  Training details and sample outputs are included in Appendices SECREF56 & SECREF57  
We divide these models into two categories and measure correlation separately for both the categories  The first category, WikiBio-Systems, includes one model each from the four families listed in Table TABREF32   This category tests whether a metric can be used to compare different model families with a large variation in the quality of their outputs  The second category, WikiBio-Hyperparams, includes 13 different hyperparameter settings of PG-Net BIBREF19 , which was the best performing system overall  9 of these were obtained by varying the beam size and length normalization penalty of the decoder network BIBREF23 , and the remaining 4 were obtained by re-scoring beams of size 8 with the information extraction model described in § SECREF4   All the models in this category produce high quality fluent texts, and differ primarily on the quantity and accuracy of the information they express  Here we are testing whether a metric can be used to compare similar systems with a small variation in performance  This is an important use-case as metrics are often used to tune hyperparameters of a model 
Human Evaluation
We collected human judgments on the quality of the 16 models trained for WikiBio, plus the reference texts  Workers on a crowd-sourcing platform, proficient in English, were shown a table with pairs of generated texts, or a generated text and the reference, and asked to select the one they prefer  Figure FIGREF34 shows the instructions they were given  Paired comparisons have been shown to be superior to rating scales for comparing generated texts BIBREF24   However, for measuring correlation the comparisons need to be aggregated into real-valued scores, INLINEFORM0 , for each of the INLINEFORM1 models  For this, we use Thurstone's method BIBREF22 , which assigns a score to each model based on how many times it was preferred over an alternative 
The data collection was performed separately for models in the WikiBio-Systems and WikiBio-Hyperparams categories  1100 tables were sampled from the development set, and for each table we got 8 different sentence pairs annotated across the two categories, resulting in a total of 8800 pairwise comparisons  Each pair was judged by one worker only which means there may be noise at the instance-level, but the aggregated system-level scores had low variance (cf  Table TABREF32 )  In total around 500 different workers were involved in the annotation  References were also included in the evaluation, and they received a lower score than PG-Net, highlighting the divergence in WikiBio 
Compared Metrics
Text only: We compare BLEU BIBREF8 , ROUGE BIBREF9 , METEOR BIBREF18 , CIDEr and CIDEr-D BIBREF25 using their publicly available implementations 
Information Extraction based: We compare the CS, RG and RG-F metrics discussed in § SECREF4  
Text & Table: We compare a variant of BLEU, denoted as BLEU-T, where the values from the table are used as additional references  BLEU-T draws inspiration from iBLEU BIBREF26 but instead rewards n-grams which match the table rather than penalizing them  For PARENT, we compare both the word-overlap model (PARENT-W) and the co-occurrence model (PARENT-C) for determining entailment  We also compare versions where a single INLINEFORM0 is tuned on the entire dataset to maximize correlation with human judgments, denoted as PARENT*-W/C 
Correlation Comparison
We use bootstrap sampling (500 iterations) over the 1100 tables for which we collected human annotations to get an idea of how the correlation of each metric varies with the underlying data  In each iteration, we sample with replacement, tables along with their references and all the generated texts for that table  Then we compute aggregated human evaluation and metric scores for each of the models and compute the correlation between the two  We report the average correlation across all bootstrap samples for each metric in Table TABREF37   The distribution of correlations for the best performing metrics are shown in Figure FIGREF38  
Table TABREF37 also indicates whether PARENT is significantly better than a baseline metric  BIBREF21 suggest using the William's test for this purpose, but since we are computing correlations between only 4/13 systems at a time, this test has very weak power in our case  Hence, we use the bootstrap samples to obtain a INLINEFORM0 confidence interval of the difference in correlation between PARENT and any other metric and check whether this is above 0 BIBREF27  
Correlations are higher for the systems category than the hyperparams category  The latter is a more difficult setting since very similar models are compared, and hence the variance of the correlations is also high  Commonly used metrics which only rely on the reference (BLEU, ROUGE, METEOR, CIDEr) have only weak correlations with human judgments  In the hyperparams category, these are often negative, implying that tuning models based on these may lead to selecting worse models  BLEU performs the best among these, and adding n-grams from the table as references improves this further (BLEU-T)
Context 3: Introduction
This work is licensed under a Creative Commons Attribution 4 0 International License  License details: http://creativecommons org/licenses/by/4 0/
Entity linking (EL), mapping entity mentions in texts to a given knowledge base (KB), serves as a fundamental role in many fields, such as question answering BIBREF0 , semantic search BIBREF1 , and information extraction BIBREF2 , BIBREF3   However, this task is non-trivial because entity mentions are usually ambiguous  As shown in Figure FIGREF1 , the mention England refers to three entities in KB, and an entity linking system should be capable of identifying the correct entity as England cricket team rather than England and England national football team 
Entity linking is typically broken down into two main phases: (i) candidate generation obtains a set of referent entities in KB for each mention, and (ii) named entity disambiguation selects the possible candidate entity by solving a ranking problem  The key challenge lies in the ranking model that computes the relevance between candidates and the corresponding mentions based on the information both in texts and KBs BIBREF4   In terms of the features used for ranking, we classify existing EL models into two groups: local models to resolve mentions independently relying on textual context information from the surrounding words BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , and global (collective) models, which are the main focus of this paper, that encourage the target entities of all mentions in a document to be topically coherent BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14  
Global models usually build an entity graph based on KBs to capture coherent entities for all identified mentions in a document, where the nodes are entities, and edges denote their relations  The graph provides highly discriminative semantic signals (e g , entity relatedness) that are unavailable to local model BIBREF15   For example (Figure FIGREF1 ), an EL model seemly cannot find sufficient disambiguation clues for the mention England from its surrounding words, unless it utilizes the coherence information of consistent topic “cricket" among adjacent mentions England, Hussain, and Essex  Although the global model has achieved significant improvements, its limitation is threefold:
To mitigate the first limitation, recent EL studies introduce neural network (NN) models due to its amazing feature abstraction and generalization ability  In such models, words/entities are represented by low dimensional vectors in a continuous space, and features for mention as well as candidate entities are automatically learned from data BIBREF4   However, existing NN-based methods for EL are either local models BIBREF16 , BIBREF17 or merely use word/entity embeddings for feature extraction and rely on another modules for collective disambiguation, which thus cannot fully utilize the power of NN models for collective EL BIBREF18 , BIBREF19 , BIBREF20  
The second drawback of the global approach has been alleviated through approximate optimization techniques, such as PageRank/random walks BIBREF21 , graph pruning BIBREF22 , ranking SVMs BIBREF23 , or loopy belief propagation (LBP) BIBREF18 , BIBREF24   However, these methods are not differentiable and thus difficult to be integrated into neural network models (the solution for the first limitation) 
To overcome the third issue of inadequate training data, BIBREF17 has explored a massive amount of hyperlinks in Wikipedia, but these potential annotations for EL contain much noise, which may distract a naive disambiguation model BIBREF6  
In this paper, we propose a novel Neural Collective Entity Linking model (NCEL), which performs global EL combining deep neural networks with Graph Convolutional Network (GCN) BIBREF25 , BIBREF26 that allows flexible encoding of entity graphs  It integrates both local contextual information and global interdependence of mentions in a document, and is efficiently trainable in an end-to-end fashion  Particularly, we introduce attention mechanism to robustly model local contextual information by selecting informative words and filtering out the noise  On the other hand, we apply GCNs to improve discriminative signals of candidate entities by exploiting the rich structure underlying the correct entities  To alleviate the global computations, we propose to convolute on the subgraph of adjacent mentions  Thus, the overall coherence shall be achieved in a chain-like way via a sliding window over the document  To the best of our knowledge, this is the first effort to develop a unified model for neural collective entity linking 
In experiments, we first verify the efficiency of NCEL via theoretically comparing its time complexity with other collective alternatives  Afterwards, we train our neural model using collected Wikipedia hyperlinks instead of dataset-specific annotations, and perform evaluations on five public available benchmarks  The results show that NCEL consistently outperforms various baselines with a favorable generalization ability  Finally, we further present the performance on a challenging dataset WW BIBREF19 as well as qualitative results, investigating the effectiveness of each key module 
Preliminaries and Framework
We denote INLINEFORM0 as a set of entity mentions in a document INLINEFORM1 , where INLINEFORM2 is either a word INLINEFORM3 or a mention INLINEFORM4   INLINEFORM5 is the entity graph for document INLINEFORM6 derived from the given knowledge base, where INLINEFORM7 is a set of entities, INLINEFORM8 denotes the relatedness between INLINEFORM9 and higher values indicate stronger relations  Based on INLINEFORM10 , we extract a subgraph INLINEFORM11 for INLINEFORM12 , where INLINEFORM13 denotes the set of candidate entities for INLINEFORM14   Note that we don't include the relations among candidates of the same mention in INLINEFORM15 because these candidates are mutually exclusive in disambiguation 
Formally, we define the entity linking problem as follows: Given a set of mentions INLINEFORM0 in a document INLINEFORM1 , and an entity graph INLINEFORM2 , the goal is to find an assignment INLINEFORM3  
To collectively find the best assignment, NCEL aims to improve the discriminability of candidates' local features by using entity relatedness within a document via GCN, which is capable of learning a function of features on the graph through shared parameters over all nodes  Figure FIGREF10 shows the framework of NCEL including three main components:
Example As shown in Figure FIGREF10 , for the current mention England, we utilize its surrounding words as local contexts (e g , surplus), and adjacent mentions (e g , Hussian) as global information  Collectively, we utilize the candidates of England INLINEFORM0 as well as those entities of its adjacencies INLINEFORM1 to construct feature vectors for INLINEFORM2 and the subgraph of relatedness as inputs of our neural model  Let darker blue indicate higher probability of being predicted, the correct candidate INLINEFORM3 becomes bluer due to its bluer neighbor nodes of other mentions INLINEFORM4   The dashed lines denote entity relations that have indirect impacts through the sliding adjacent window , and the overall structure shall be achieved via multiple sub-graphs by traversing all mentions 
Before introducing our model, we first describe the component of candidate generation 
Candidate Generation
Similar to previous work BIBREF24 , we use the prior probability INLINEFORM0 of entity INLINEFORM1 conditioned on mention INLINEFORM2 both as a local feature and to generate candidate entities: INLINEFORM3   We compute INLINEFORM4 based on statistics of mention-entity pairs from: (i) Wikipedia page titles, redirect titles and hyperlinks, (ii) the dictionary derived from a large Web Corpus BIBREF27 , and (iii) the YAGO dictionary with a uniform distribution BIBREF22   We pick up the maximal prior if a mention-entity pair occurs in different resources  In experiments, to optimize for memory and run time, we keep only top INLINEFORM5 entities based on INLINEFORM6   In the following two sections, we will present the key components of NECL, namely feature extraction and neural network for collective entity linking 
Feature Extraction
The main goal of NCEL is to find a solution for collective entity linking using an end-to-end neural model, rather than to improve the measurements of local textual similarity or global mention/entity relatedness  Therefore, we use joint embeddings of words and entities at sense level BIBREF28 to represent mentions and its contexts for feature extraction  In this section, we give a brief description of our embeddings followed by our features used in the neural model 
Learning Joint Embeddings of Word and Entity
Following BIBREF28 , we use Wikipedia articles, hyperlinks, and entity outlinks to jointly learn word/mention and entity embeddings in a unified vector space, so that similar words/mentions and entities have similar vectors  To address the ambiguity of words/mentions, BIBREF28 represents each word/mention with multiple vectors, and each vector denotes a sense referring to an entity in KB  The quality of the embeddings is verified on both textual similarity and entity relatedness tasks 
Formally, each word/mention has a global embedding INLINEFORM0 , and multiple sense embeddings INLINEFORM1   Each sense embedding INLINEFORM2 refers to an entity embedding INLINEFORM3 , while the difference between INLINEFORM4 and INLINEFORM5 is that INLINEFORM6 models the co-occurrence information of an entity in texts (via hyperlinks) and INLINEFORM7 encodes the structured entity relations in KBs  More details can be found in the original paper 
Local Features
Local features focus on how compatible the entity is mentioned in a piece of text (i e , the mention and the context words)  Except for the prior probability (Section SECREF9 ), we define two types of local features for each candidate entity INLINEFORM0 :
String Similarity Similar to BIBREF16 , we define string based features as follows: the edit distance between mention's surface form and entity title, and boolean features indicating whether they are equivalent, whether the mention is inside, starts with or ends with entity title and vice versa 
Compatibility We also measure the compatibility of INLINEFORM0 with the mention's context words INLINEFORM1 by computing their similarities based on joint embeddings: INLINEFORM2 and INLINEFORM3 , where INLINEFORM4 is the context embedding of INLINEFORM5 conditioned on candidate INLINEFORM6 and is defined as the average sum of word global vectors weighted by attentions: INLINEFORM7
where INLINEFORM0 is the INLINEFORM1 -th word's attention from INLINEFORM2   In this way, we automatically select informative words by assigning higher attention weights, and filter out irrelevant noise through small weights  The attention INLINEFORM3 is computed as follows: INLINEFORM4
where INLINEFORM0 is the similarity measurement, and we use cosine similarity in the presented work  We concatenate the prior probability, string based similarities, compatibility similarities and the embeddings of contexts as well as the entity as the local feature vectors 
Global Features
The key idea of collective EL is to utilize the topical coherence throughout the entire document  The consistency assumption behind it is that: all mentions in a document shall be on the same topic  However, this leads to exhaustive computations if the number of mentions is large  Based on the observation that the consistency attenuates along with the distance between two mentions, we argue that the adjacent mentions might be sufficient for supporting the assumption efficiently 
Formally, we define neighbor mentions as INLINEFORM0 adjacent mentions before and after current mention INLINEFORM1 : INLINEFORM2 , where INLINEFORM3 is the pre-defined window size  Thus, the topical coherence at document level shall be achieved in a chain-like way  As shown in Figure FIGREF10 ( INLINEFORM4 ), mentions Hussain and Essex, a cricket player and the cricket club, provide adequate disambiguation clues to induce the underlying topic “cricket" for the current mention England, which impacts positively on identifying the mention surrey as another cricket club via the common neighbor mention Essex 
A degraded case happens if INLINEFORM0 is large enough to cover the entire document, and the mentions used for global features become the same as the previous work, such as BIBREF21   In experiments, we heuristically found a suitable INLINEFORM1 which is much smaller than the total number of mentions  The benefits of efficiency are in two ways: (i) to decrease time complexity, and (ii) to trim the entity graph into a fixed size of subgraph that facilitates computation acceleration through GPUs and batch techniques, which will be discussed in Section SECREF24  
Given neighbor mentions INLINEFORM0 , we extract two types of vectorial global features and structured global features for each candidate INLINEFORM1 :
Neighbor Mention Compatibility Suppose neighbor mentions are topical coherent, a candidate entity shall also be compatible with neighbor mentions if it has a high compatibility score with the current mention, otherwise not  That is, we extract the vectorial global features by computing the similarities between INLINEFORM0 and all neighbor mentions: INLINEFORM1 , where INLINEFORM2 is the mention embedding by averaging the global vectors of words in its surface form: INLINEFORM3 , where INLINEFORM4 are tokenized words of mention INLINEFORM5  
Subgraph Structure The above features reflect the consistent semantics in texts (i e , mentions)  We now extract structured global features using the relations in KB, which facilitates the inference among candidates to find the most topical coherent subset

ground_truth: 1,268 randomly selected tweets in the Stanford Sentiment Analysis Dataset BIBREF36, all 12,594 unique English hashtags and their associated tweets from the same Stanford dataset

Stanford Sentiment Analysis Dataset BIBREF36

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. Ngrams of which length are aligned using PARENT?
Context 2: 
Among the extractive evaluation metrics, CS, which also only relies on the reference, has poor correlation in the hyperparams category  RG-F, and both variants of the PARENT metric achieve the highest correlation for both settings  There is no significant difference among these for the hyperparams category, but for systems, PARENT-W is significantly better than the other two  While RG-F needs a full information extraction pipeline in its implementation, PARENT-C only relies on co-occurrence counts, and PARENT-W can be used out-of-the-box for any dataset  To our knowledge, this is the first rigorous evaluation of using information extraction for generation evaluation 
On this dataset, the word-overlap model showed higher correlation than the co-occurrence model for entailment  In § SECREF47 we will show that for the WebNLG dataset, where more paraphrasing is involved between the table and text, the opposite is true  Lastly, we note that the heuristic for selecting INLINEFORM0 is sufficient to produce high correlations for PARENT, however, if human annotations are available, this can be tuned to produce significantly higher correlations (PARENT*-W/C) 
Analysis
In this section we further analyze the performance of PARENT-W under different conditions, and compare to the other best metrics from Table TABREF37  
To study the correlation as we vary the number of divergent references, we also collected binary labels from workers for whether a reference is entailed by the corresponding table  We define a reference as entailed when it mentions only information which can be inferred from the table  Each table and reference pair was judged by 3 independent workers, and we used the majority vote as the label for that pair  Overall, only INLINEFORM0 of the references were labeled as entailed by the table  Fleiss' INLINEFORM1 was INLINEFORM2 , which indicates a fair agreement  We found the workers sometimes disagreed on what information can be reasonably entailed by the table 
Figure FIGREF40 shows the correlations as we vary the percent of entailed examples in the evaluation set of WikiBio  Each point is obtained by fixing the desired proportion of entailed examples, and sampling subsets from the full set which satisfy this proportion  PARENT and RG-F remain stable and show a high correlation across the entire range, whereas BLEU and BLEU-T vary a lot  In the hyperparams category, the latter two have the worst correlation when the evaluation set contains only entailed examples, which may seem surprising  However, on closer examination we found that this subset tends to omit a lot of information from the tables  Systems which produce more information than these references are penalized by BLEU, but not in the human evaluation  PARENT overcomes this issue by measuring recall against the table in addition to the reference 
We check how different components in the computation of PARENT contribute to its correlation to human judgments  Specifically, we remove the probability INLINEFORM0 of an n-gram INLINEFORM1 being entailed by the table from Eqs  EQREF19 and EQREF23   The average correlation for PARENT-W drops to INLINEFORM5 in this case  We also try a variant of PARENT with INLINEFORM6 , which removes the contribution of Table Recall (Eq  EQREF22 )  The average correlation is INLINEFORM7 in this case  With these components, the correlation is INLINEFORM8 , showing that they are crucial to the performance of PARENT 
BIBREF28 point out that hill-climbing on an automatic metric is meaningless if that metric has a low instance-level correlation to human judgments  In Table TABREF46 we show the average accuracy of the metrics in making the same judgments as humans between pairs of generated texts  Both variants of PARENT are significantly better than the other metrics, however the best accuracy is only INLINEFORM0 for the binary task  This is a challenging task, since there are typically only subtle differences between the texts  Achieving higher instance-level accuracies will require more sophisticated language understanding models for evaluation 
WebNLG Dataset
To check how PARENT correlates with human judgments when the references are elicited from humans (and less likely to be divergent), we check its correlation with the human ratings provided for the systems competing in the WebNLG challenge BIBREF6   The task is to generate text describing 1-5 RDF triples (e g  John E Blaha, birthPlace, San Antonio), and human ratings were collected for the outputs of 9 participating systems on 223 instances  These systems include a mix of pipelined, statistical and neural methods  Each instance has upto 3 reference texts associated with the RDF triples, which we use for evaluation 
The human ratings were collected on 3 distinct aspects – grammaticality, fluency and semantics, where semantics corresponds to the degree to which a generated text agrees with the meaning of the underlying RDF triples  We report the correlation of several metrics with these ratings in Table TABREF48   Both variants of PARENT are either competitive or better than the other metrics in terms of the average correlation to all three aspects  This shows that PARENT is applicable for high quality references as well 
While BLEU has the highest correlation for the grammar and fluency aspects, PARENT does best for semantics  This suggests that the inclusion of source tables into the evaluation orients the metric more towards measuring the fidelity of the content of the generation  A similar trend is seen comparing BLEU and BLEU-T  As modern neural text generation systems are typically very fluent, measuring their fidelity is of increasing importance  Between the two entailment models, PARENT-C is better due to its higher correlation with the grammaticality and fluency aspects 
The INLINEFORM0 parameter in the calculation of PARENT decides whether to compute recall against the table or the reference (Eq  EQREF22 )  Figure FIGREF50 shows the distribution of the values taken by INLINEFORM1 using the heuristic described in § SECREF3 for instances in both WikiBio and WebNLG  For WikiBio, the recall of the references against the table is generally low, and hence the recall of the generated text relies more on the table  For WebNLG, where the references are elicited from humans, this recall is much higher (often INLINEFORM2 ), and hence the recall of the generated text relies more on the reference 
Related Work
Over the years several studies have evaluated automatic metrics for measuring text generation performance BIBREF29 , BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 , BIBREF34 , BIBREF35   The only consensus from these studies seems to be that no single metric is suitable across all tasks  A recurring theme is that metrics like BLEU and NIST BIBREF36 are not suitable for judging content quality in NLG  Recently, BIBREF37 did a comprehensive study of several metrics on the outputs of state-of-the-art NLG systems, and found that while they showed acceptable correlation with human judgments at the system level, they failed to show any correlation at the sentence level  Ours is the first study which checks the quality of metrics when table-to-text references are divergent  We show that in this case even system level correlations can be unreliable 
Hallucination BIBREF38 , BIBREF39 refers to when an NLG system generates text which mentions extra information than what is present in the source from which it is generated  Divergence can be viewed as hallucination in the reference text itself  PARENT deals with hallucination by discounting n-grams which do not overlap with either the reference or the table 
PARENT draws inspiration from iBLEU BIBREF26 , a metric for evaluating paraphrase generation, which compares the generated text to both the source text and the reference  While iBLEU penalizes texts which match the source, here we reward such texts since our task values accuracy of generated text more than the need for paraphrasing the tabular content BIBREF40   Similar to SARI for text simplification BIBREF41 and Q-BLEU for question generation BIBREF42 , PARENT falls under the category of task-specific metrics 
Conclusions
We study the automatic evaluation of table-to-text systems when the references diverge from the table  We propose a new metric, PARENT, which shows the highest correlation with humans across a range of settings with divergent references in WikiBio  We also perform the first empirical evaluation of information extraction based metrics BIBREF1 , and find RG-F to be effective  Lastly, we show that PARENT is comparable to the best existing metrics when references are elicited by humans on the WebNLG data 
Acknowledgements
Bhuwan Dhingra is supported by a fellowship from Siemens, and by grants from Google  We thank Maruan Al-Shedivat, Ian Tenney, Tom Kwiatkowski, Michael Collins, Slav Petrov, Jason Baldridge, David Reitter and other members of the Google AI Language team for helpful discussions and suggestions  We thank Sam Wiseman for sharing data for an earlier version of this paper  We also thank the anonymous reviewers for their feedback 
Information Extraction System
For evaluation via information extraction BIBREF1 we train a model for WikiBio which accepts text as input and generates a table as the output  Tables in WikiBio are open-domain, without any fixed schema for which attributes may be present or absent in an instance  Hence we employ the Pointer-Generator Network (PG-Net) BIBREF19 for this purpose  Specifically, we use a sequence-to-sequence model, whose encoder and decoder are both single-layer bi-directional LSTMs  The decoder is augmented with an attention mechanism over the states of the encoder  Further, it also uses a copy mechanism to optionally copy tokens directly from the source text  We do not use the coverage mechanism of BIBREF19 since that is specific to the task of summarization they study  The decoder is trained to produce a linearized version of the table where the rows and columns are flattened into a sequence, and separate by special tokens  Figure FIGREF53 shows an example 
Clearly, since the references are divergent, the model cannot be expected to produce the entire table, and we see some false information being hallucinated after training  Nevertheless, as we show in § SECREF36 , this system can be used for evaluating generated texts  After training, we can parse the output sequence along the special tokens INLINEFORM0 R INLINEFORM1 and INLINEFORM2 C INLINEFORM3 to get a set of (attribute, value) pairs  Table TABREF54 shows the precision, recall and F-score of these extracted pairs against the ground truth tables, where the attributes and values are compared using an exact string match 
Hyperparameters
After tuning we found the same set of hyperparameters to work well for both the table-to-text PG-Net, and the inverse information extraction PG-Net  The hidden state size of the biLSTMs was set to 200  The input and output vocabularies were set to 50000 most common words in the corpus, with additional special symbols for table attribute names (such as “birth-date”)  The embeddings of the tokens in the vocabulary were initialized with Glove BIBREF43   Learning rate of INLINEFORM0 was used during training, with the Adam optimizer, and a dropout of INLINEFORM1 was also applied to the outputs of the biLSTM  Models were trained till the loss on the dev set stopped dropping  Maximum length of a decoded text was set to 40 tokens, and that of the tables was set to 120 tokens  Various beam sizes and length normalization penalties were applied for the table-to-text system, which are listed in the main paper  For the information extraction system, we found a beam size of 8 and no length penalty to produce the highest F-score on the dev set 
Sample Outputs
Table TABREF55 shows some sample references and the corresponding predictions from the best performing model, PG-Net for WikiBio 

ground_truth: Unanswerable

Answer with content missing: (Parent subsections) combine precisions for n-gram orders 1-4

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 effective is their NCEL approach overall?
Context 3:  For each document, we obtain the entity graph INLINEFORM0 by taking candidate entities of all mentions INLINEFORM1 as nodes, and using entity embeddings to compute their similarities as edges INLINEFORM2   Then, we extract the subgraph structured features INLINEFORM3 for each entity INLINEFORM4 for efficiency 
Formally, we define the subgraph as: INLINEFORM0 , where INLINEFORM1   For example (Figure FIGREF1 ), for entity England cricket team, the subgraph contains the relation from it to all candidates of neighbor mentions: England cricket team, Nasser Hussain (rugby union), Nasser Hussain, Essex, Essex County Cricket Club and Essex, New York  To support batch-wise acceleration, we represent INLINEFORM2 in the form of adjacency table based vectors: INLINEFORM3 , where INLINEFORM4 is the number of candidates per mention 
Finally, for each candidate INLINEFORM0 , we concatenate local features and neighbor mention compatibility scores as the feature vector INLINEFORM1 , and construct the subgraph structure representation INLINEFORM2 as the inputs of NCEL 
Neural Collective Entity Linking
NCEL incorporates GCN into a deep neural network to utilize structured graph information for collectively feature abstraction, while differs from conventional GCN in the way of applying the graph  Instead of the entire graph, only a subset of nodes is “visible" to each node in our proposed method, and then the overall structured information shall be reached in a chain-like way  Fixing the size of the subset, NCEL is further speeded up by batch techniques and GPUs, and is efficient to large-scale data 
Graph Convolutional Network
GCNs are a type of neural network model that deals with structured data  It takes a graph as an input and output labels for each node  As a simplification of spectral graph convolutions, the main idea of BIBREF26 is similar to a propagation model: to enhance the features of a node according to its neighbor nodes  The formulation is as follows: INLINEFORM0
where INLINEFORM0 is a normalized adjacent matrix of the input graph with self-connection, INLINEFORM1 and INLINEFORM2 are the hidden states and weights in the INLINEFORM3 -th layer, and INLINEFORM4 is a non-linear activation, such as ReLu 
Model Architecture
As shown in Figure FIGREF10 , NCEL identifies the correct candidate INLINEFORM0 for the mention INLINEFORM1 by using vectorial features as well as structured relatedness with candidates of neighbor mentions INLINEFORM2   Given feature vector INLINEFORM3 and subgraph representation INLINEFORM4 of each candidate INLINEFORM5 , we stack them as inputs for mention INLINEFORM6 : INLINEFORM7 , and the adjacent matrix INLINEFORM8 , where INLINEFORM9 denotes the subgraph with self-connection  We normalize INLINEFORM10 such that all rows sum to one, denoted as INLINEFORM11 , avoiding the change in the scale of the feature vectors 
Given INLINEFORM0 and INLINEFORM1 , the goal of NCEL is to find the best assignment: INLINEFORM2
where INLINEFORM0 is the output variable of candidates, and INLINEFORM1 is a probability function as follows: INLINEFORM2
where INLINEFORM0 is the score function parameters by INLINEFORM1   NCEL learns the mapping INLINEFORM2 through a neural network including three main modules: encoder, sub-graph convolution network (sub-GCN) and decoder  Next, we introduce them in turn 
Encoder The function of this module is to integrate different features by a multi-layer perceptron (MLP): INLINEFORM0
where INLINEFORM0 is the hidden states of the current mention, INLINEFORM1 and INLINEFORM2 are trainable parameters and bias  We use ReLu as the non-linear activation INLINEFORM3  
Sub-Graph Convolution Network Similar to GCN, this module learns to abstract features from the hidden state of the mention itself as well as its neighbors  Suppose INLINEFORM0 is the hidden states of the neighbor INLINEFORM1 , we stack them to expand the current hidden states of INLINEFORM2 as INLINEFORM3 , such that each row corresponds to that in the subgraph adjacent matrix INLINEFORM4   We define sub-graph convolution as: INLINEFORM5
where INLINEFORM0 is a trainable parameter 
Decoder After INLINEFORM0 iterations of sub-graph convolution, the hidden states integrate both features of INLINEFORM1 and its neighbors  A fully connected decoder maps INLINEFORM2 to the number of candidates as follows: INLINEFORM3
where INLINEFORM0  
Training
The parameters of network are trained to minimize cross-entropy of the predicted and ground truth INLINEFORM0 : INLINEFORM1
Suppose there are INLINEFORM0 documents in training corpus, each document has a set of mentions INLINEFORM1 , leading to totally INLINEFORM2 mention sets  The overall objective function is as follows: INLINEFORM3
Experiments
To avoid overfitting with some dataset, we train NCEL using collected Wikipedia hyperlinks instead of specific annotated data  We then evaluate the trained model on five different benchmarks to verify the linking precision as well as the generalization ability  Furthermore, we investigate the effectiveness of key modules in NCEL and give qualitative results for comprehensive analysis 
Baselines and Datasets
We compare NCEL with the following state-of-the-art EL methods including three local models and three types of global models:
Local models: He BIBREF29 and Chisholm BIBREF6 beat many global models by using auto-encoders and web links, respectively, and NTEE BIBREF16 achieves the best performance based on joint embeddings of words and entities 
Iterative model: AIDA BIBREF22 links entities by iteratively finding a dense subgraph 
Loopy Belief Propagation: Globerson BIBREF18 and PBoH BIBREF30 introduce LBP BIBREF31 techniques for collective inference, and Ganea BIBREF24 solves the global training problem via truncated fitting LBP 
PageRank/Random Walk: Boosting BIBREF32 , AGDISTISG BIBREF33 , Babelfy BIBREF34 , WAT BIBREF35 , xLisa BIBREF36 and WNED BIBREF19 performs PageRank BIBREF37 or random walk BIBREF38 on the mention-entity graph and use the convergence score for disambiguation 
For fairly comparison, we report the original scores of the baselines in the papers  Following these methods, we evaluate NCEL on the following five datasets: (1) CoNLL-YAGO BIBREF22 : the CoNLL 2003 shared task including testa of 4791 mentions in 216 documents, and testb of 4485 mentions in 213 documents  (2) TAC2010 BIBREF39 : constructed for the Text Analysis Conference that comprises 676 mentions in 352 documents for testing  (3) ACE2004 BIBREF23 : a subset of ACE2004 co-reference documents including 248 mentions in 35 documents, which is annotated by Amazon Mechanical Turk  (4) AQUAINT BIBREF40 : 50 news articles including 699 mentions from three different news agencies  (5) WW BIBREF19 : a new benchmark with balanced prior distributions of mentions, leading to a hard case of disambiguation  It has 6374 mentions in 310 documents automatically extracted from Wikipedia 
Training Details and Running Time Analysis
Training We collect 50,000 Wikipedia articles according to the number of its hyperlinks as our training data  For efficiency, we trim the articles to the first three paragraphs leading to 1,035,665 mentions in total  Using CoNLL-Test A as the development set, we evaluate the trained NCEL on the above benchmarks  We set context window to 20, neighbor mention window to 6, and top INLINEFORM0 candidates for each mention  We use two layers with 2000 and 1 hidden units in MLP encoder, and 3 layers in sub-GCN  We use early stop and fine tune the embeddings  With a batch size of 16, nearly 3 epochs cost less than 15 minutes on the server with 20 core CPU and the GeForce GTX 1080Ti GPU with 12Gb memory  We use standard Precision, Recall and F1 at mention level (Micro) and at the document level (Macro) as measurements 
Complexity Analysis Compared with local methods, the main disadvantage of collective methods is high complexity and expensive costs  Suppose there are INLINEFORM0 mentions in documents on average, among these global models, NCEL not surprisingly has the lowest time complexity INLINEFORM1 since it only considers adjacent mentions, where INLINEFORM2 is the number of sub-GCN layers indicating the iterations until convergence  AIDA has the highest time complexity INLINEFORM3 in worst case due to exhaustive iteratively finding and sorting the graph  The LBP and PageRank/random walk based methods achieve similar high time complexity of INLINEFORM4 mainly because of the inference on the entire graph 
Results on GERBIL
GERBIL BIBREF41 is a benchmark entity annotation framework that aims to provide a unified comparison among different EL methods across datasets including ACE2004, AQUAINT and CoNLL  We compare NCEL with the global models that report the performance on GERBIL 
As shown in Table TABREF26 , NCEL achieves the best performance in most cases with an average gain of 2% on Micro F1 and 3% Macro F1  The baseline methods also achieve competitive results on some datasets but fail to adapt to the others  For example, AIDA and xLisa perform quite well on ACE2004 but poorly on other datasets, or WAT, PBoH, and WNED have a favorable performance on CoNLL but lower values on ACE2004 and AQUAINT  Our proposed method performs consistently well on all datasets that demonstrates the good generalization ability 
Results on TAC2010 and WW
In this section, we investigate the effectiveness of NCEL in the “easy" and “hard" datasets, respectively  Particularly, TAC2010, which has two mentions per document on average (Section SECREF19 ) and high prior probabilities of correct candidates (Figure FIGREF28 ), is regarded as the “easy" case for EL, and WW is the “hard" case since it has the most mentions with balanced prior probabilities BIBREF19   Besides, we further compare the impact of key modules by removing the following part from NCEL: global features (NCEL-local), attention (NCEL-noatt), embedding features (NCEL-noemb), and the impact of the prior probability (prior) 
The results are shown in Table FIGREF28 and Table FIGREF28   We can see the average linking precision (Micro) of WW is lower than that of TAC2010, and NCEL outperforms all baseline methods in both easy and hard cases  In the “easy" case, local models have similar performance with global models since only little global information is available (2 mentions per document)  Besides, NN-based models, NTEE and NCEL-local, perform significantly better than others including most global models, demonstrating that the effectiveness of neural models deals with the first limitation in the introduction 
Impact of NCEL Modules
As shown in Figure FIGREF28 , the prior probability performs quite well in TAC2010 but poorly in WW  Compared with NCEL-local, the global module in NCEL brings more improvements in the “hard" case than that for “easy" dataset, because local features are discriminative enough in most cases of TAC2010, and global information becomes quite helpful when local features cannot handle  That is, our propose collective model is robust and shows a good generalization ability to difficult EL  The improvements by each main module are relatively small in TAC2010, while the modules of attention and embedding features show non-negligible impacts in WW (even worse than local model), mainly because WW contains much noise, and these two modules are effective in improving the robustness to noise and the ability of generalization by selecting informative words and providing more accurate semantics, respectively 
Qualitative Analysis
The results of example in Figure FIGREF1 are shown in Table TABREF30 , which is from CoNLL testa dataset  For mention Essex, although both NCEL and NCEL-local correctly identify entity Essex County Cricket Club, NCEL outputs higher probability due to the enhancement of neighbor mentions  Moreover, for mention England, NCEL-local cannot find enough disambiguation clues from its context words, such as surplus and requirements, and thus assigns a higher probability of 0 42 to the country England according to the prior probability  Collectively, NCEL correctly identifies England cricket team with a probability of 0 72 as compared with 0 20 in NCEL-local with the help of its neighbor mention Essex 
Conclusion
In this paper, we propose a neural model for collective entity linking that is end-to-end trainable  It applies GCN on subgraphs instead of the entire entity graph to efficiently learn features from both local and global information  We design an attention mechanism that endows NCEL robust to noisy data  Trained on collected Wikipedia hyperlinks, NCEL outperforms the state-of-the-art collective methods across five different datasets  Besides, further analysis of the impacts of main modules as well as qualitative results demonstrates its effectiveness 
In the future, we will extend our method into cross-lingual settings to help link entities in low-resourced languages by exploiting rich knowledge from high-resourced languages, and deal with NIL entities to facilitate specific applications 
Acknowledgments
The work is supported by National Key Research and Development Program of China (2017YFB1002101), NSFC key project (U1736204, 61661146007), and THUNUS NExT Co-Lab 

ground_truth: NCEL consistently outperforms various baselines with a favorable generalization ability