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

Please answer the questions based on the text provided below. Do they report results only on English data?
Context 1: Introduction
Distributed word representations, commonly referred to as word embeddings BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , serve as elementary building blocks in the course of algorithm design for an expanding range of applications in natural language processing (NLP), including named entity recognition BIBREF4 , BIBREF5 , parsing BIBREF6 , sentiment analysis BIBREF7 , BIBREF8 , and word-sense disambiguation BIBREF9   Although the empirical utility of word embeddings as an unsupervised method for capturing the semantic or syntactic features of a certain word as it is used in a given lexical resource is well-established BIBREF10 , BIBREF11 , BIBREF12 , an understanding of what these features mean remains an open problem BIBREF13 , BIBREF14 and as such word embeddings mostly remain a black box  It is desirable to be able to develop insight into this black box and be able to interpret what it means, while retaining the utility of word embeddings as semantically-rich intermediate representations  Other than the intrinsic value of this insight, this would not only allow us to explain and understand how algorithms work BIBREF15 , but also set a ground that would facilitate the design of new algorithms in a more deliberate way 
Recent approaches to generating word embeddings (e g  BIBREF0 , BIBREF2 ) are rooted linguistically in the field of distributed semantics BIBREF16 , where words are taken to assume meaning mainly by their degree of interaction (or lack thereof) with other words in the lexicon BIBREF17 , BIBREF18   Under this paradigm, dense, continuous vector representations are learned in an unsupervised manner from a large corpus, using the word cooccurrence statistics directly or indirectly, and such an approach is shown to result in vector representations that mathematically capture various semantic and syntactic relations between words BIBREF0 , BIBREF2 , BIBREF3   However, the dense nature of the learned embeddings obfuscate the distinct concepts encoded in the different dimensions, which renders the resulting vectors virtually uninterpretable  The learned embeddings make sense only in relation to each other and their specific dimensions do not carry explicit information that can be interpreted  However, being able to interpret a word embedding would illuminate the semantic concepts implicitly represented along the various dimensions of the embedding, and reveal its hidden semantic structures 
In the literature, researchers tackled interpretability problem of the word embeddings using different approaches  Several researchers BIBREF19 , BIBREF20 , BIBREF21 proposed algorithms based on non-negative matrix factorization (NMF) applied to cooccurrence variant matrices  Other researchers suggested to obtain interpretable word vectors from existing uninterpretable word vectors by applying sparse coding BIBREF22 , BIBREF23 , by training a sparse auto-encoder to transform the embedding space BIBREF24 , by rotating the original embeddings BIBREF25 , BIBREF26 or by applying transformations based on external semantic datasets BIBREF27  
Although the above-mentioned approaches provide better interpretability that is measured using a particular method such as word intrusion test, usually the improved interpretability comes with a cost of performance in the benchmark tests such as word similarity or word analogy  One possible explanation for this performance decrease is that the proposed transformations from the original embedding space distort the underlying semantic structure constructed by the original embedding algorithm  Therefore, it can be claimed that a method that learns dense and interpretable word embeddings without inflicting any damage to the underlying semantic learning mechanism is the key to achieve both high performing and interpretable word embeddings 
Especially after the introduction of the word2vec algorithm by Mikolov BIBREF0 , BIBREF1 , there has been a growing interest in algorithms that generate improved word representations under some performance metric  Significant effort is spent on appropriately modifying the objective functions of the algorithms in order to incorporate knowledge from external resources, with the purpose of increasing the performance of the resulting word representations BIBREF28 , BIBREF29 , BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 , BIBREF34 , BIBREF35 , BIBREF36 , BIBREF37   Inspired by the line of work reported in these studies, we propose to use modified objective functions for a different purpose: learning more interpretable dense word embeddings  By doing this, we aim to incorporate semantic information from an external lexical resource into the word embedding so that the embedding dimensions are aligned along predefined concepts  This alignment is achieved by introducing a modification to the embedding learning process  In our proposed method, which is built on top of the GloVe algorithm BIBREF2 , the cost function for any one of the words of concept word-groups is modified by the introduction of an additive term to the cost function  Each embedding vector dimension is first associated with a concept  For a word belonging to any one of the word-groups representing these concepts, the modified cost term favors an increase for the value of this word's embedding vector dimension corresponding to the concept that the particular word belongs to  For words that do not belong to any one of the word-groups, the cost term is left untouched  Specifically, Roget's Thesaurus BIBREF38 , BIBREF39 is used to derive the concepts and concept word-groups to be used as the external lexical resource for our proposed method  We quantitatively demonstrate the increase in interpretability by using the measure given in BIBREF27 , BIBREF40 as well as demonstrating qualitative results  We also show that the semantic structure of the original embedding has not been harmed in the process since there is no performance loss with standard word-similarity or word-analogy tests 
The paper is organized as follows  In Section SECREF2 , we discuss previous studies related to our work under two main categories: interpretability of word embeddings and joint-learning frameworks where the objective function is modified  In Section SECREF3 , we present the problem framework and provide the formulation within the GloVe BIBREF2 algorithm setting  In Section SECREF4 where our approach is proposed, we motivate and develop a modification to the original objective function with the aim of increasing representation interpretability  In Section SECREF5 , experimental results are provided and the proposed method is quantitatively and qualitatively evaluated  Additionally, in Section SECREF5 , results demonstrating the extent to which the original semantic structure of the embedding space is affected are presented by using word-analogy and word-similarity tests  We conclude the paper in Section SECREF6  
Related Work
Methodologically, our work is related to prior studies that aim to obtain “improved” word embeddings using external lexical resources, under some performance metric  Previous work in this area can be divided into two main categories: works that i) modify the word embedding learning algorithm to incorporate lexical information, ii) operate on pre-trained embeddings with a post-processing step 
Among works that follow the first approach, BIBREF28 extend the Skip-Gram model by incorporating the word similarity relations extracted from the Paraphrase Database (PPDB) and WordNet BIBREF29 , into the Skip-Gram predictive model as an additional cost term  In BIBREF30 , the authors extend the CBOW model by considering two types of semantic information, termed relational and categorical, to be incorporated into the embeddings during training  For the former type of semantic information, the authors propose the learning of explicit vectors for the different relations extracted from a semantic lexicon such that the word pairs that satisfy the same relation are distributed more homogeneously  For the latter, the authors modify the learning objective such that some weighted average distance is minimized for words under the same semantic category  In BIBREF31 , the authors represent the synonymy and hypernymy-hyponymy relations in terms of inequality constraints, where the pairwise similarity rankings over word triplets are forced to follow an order extracted from a lexical resource  Following their extraction from WordNet, the authors impose these constraints in the form of an additive cost term to the Skip-Gram formulation  Finally, BIBREF32 builds on top of the GloVe algorithm by introducing a regularization term to the objective function that encourages the vector representations of similar words as dictated by WordNet to be similar as well 
Turning our attention to the post-processing approach for enriching word embeddings with external lexical knowledge, BIBREF33 has introduced the retrofitting algorithm that acts on pre-trained embeddings such as Skip-Gram or GloVe  The authors propose an objective function that aims to balance out the semantic information captured in the pre-trained embeddings with the constraints derived from lexical resources such as WordNet, PPDB and FrameNet  One of the models proposed in BIBREF34 extends the retrofitting approach to incorporate the word sense information from WordNet  Similarly, BIBREF35 creates multi-sense embeddings by gathering the word sense information from a lexical resource and learning to decompose the pre-trained embeddings into a convex combination of sense embeddings  In BIBREF36 , the authors focus on improving word embeddings for capturing word similarity, as opposed to mere relatedness  To this end, they introduce the counter-fitting technique which acts on the input word vectors such that synonymous words are attracted to one another whereas antonymous words are repelled, where the synonymy-antonymy relations are extracted from a lexical resource  More recently, the ATTRACT-REPEL algorithm proposed by BIBREF37 improves on counter-fitting by a formulation which imparts the word vectors with external lexical information in mini-batches 
Most of the studies discussed above ( BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 , BIBREF34 , BIBREF36 , BIBREF37 ) report performance improvements in benchmark tests such as word similarity or word analogy, while BIBREF29 uses a different analysis method (mean reciprocal rank)  In sum, the literature is rich with studies aiming to obtain word embeddings that perform better under specific performance metrics  However, less attention has been directed to the issue of interpretability of the word embeddings  In the literature, the problem of interpretability has been tackled using different approaches  BIBREF19 proposed non-negative matrix factorization (NMF) for learning sparse, interpretable word vectors from co-occurrence variant matrices where the resulting vector space is called non-negative sparse embeddigns (NNSE)  However, since NMF methods require maintaining a global matrix for learning, they suffer from memory and scale issue  This problem has been addressed in BIBREF20 where an online method of learning interpretable word embeddings from corpora using a modified version of skip-gram model BIBREF0 is proposed  As a different approach, BIBREF21 combined text-based similarity information among words with brain activity based similarity information to improve interpretability using joint non-negative sparse embedding (JNNSE) 
A common alternative approach for learning interpretable embeddings is to learn transformations that map pre-trained state-of-the-art embeddings to new interpretable semantic spaces  To obtain sparse, higher dimensional and more interpretable vector spaces, BIBREF22 and BIBREF23 use sparse coding on conventional dense word embeddings  However, these methods learn the projection vectors that are used for the transformation from the word embeddings without supervision  For this reason, labels describing the corresponding semantic categories cannot be provided  An alternative approach was proposed in BIBREF25 , where orthogonal transformations were utilized to increase interpretability while preserving the performance of the underlying embedding  However, BIBREF25 has also shown that total interpretability of an embedding is kept constant under any orthogonal transformation and it can only be redistributed across the dimensions  Rotation algorithms based on exploratory factor analysis (EFA) to preserve the performance of the original word embeddings while improving their interpretability was proposed in BIBREF26   BIBREF24 proposed to deploy a sparse auto-encoder using pre-trained dense word embeddings to improve interpretability  More detailed investigation of semantic structure and interpretability of word embeddings can be found in BIBREF27 , where a metric was proposed to quantitatively measure the degree of interpretability already present in the embedding vector spaces 
Previous works on interpretability mentioned above, except BIBREF21 , BIBREF27 and our proposed method, do not need external resources, utilization of which has both advantages and disadvantages  Methods that do not use external resources require fewer resources but they also lack the aid of information extracted from these resources 
Problem Description
For the task of unsupervised word embedding extraction, we operate on a discrete collection of lexical units (words) INLINEFORM0 that is part of an input corpus INLINEFORM1 , with number of tokens INLINEFORM2 , sourced from a vocabulary INLINEFORM3 of size INLINEFORM4   In the setting of distributional semantics, the objective of a word embedding algorithm is to maximize some aggregate utility over the entire corpus so that some measure of “closeness” is maximized for pairs of vector representations INLINEFORM14 for words which, on the average, appear in proximity to one another  In the GloVe algorithm BIBREF2 , which we base our improvements upon, the following objective function is considered: DISPLAYFORM0
In ( EQREF6 ), INLINEFORM0 and INLINEFORM1 stand for word and context vector representations, respectively, for words INLINEFORM2 and INLINEFORM3 , while INLINEFORM4 represents the (possibly weighted) cooccurrence count for the word pair INLINEFORM5   Intuitively, ( EQREF6 ) represents the requirement that if some word INLINEFORM6 occurs often enough in the context (or vicinity) of another word INLINEFORM7 , then the corresponding word representations should have a large enough inner product in keeping with their large INLINEFORM8 value, up to some bias terms INLINEFORM9 ; and vice versa  INLINEFORM10 in ( EQREF6 ) is used as a discounting factor that prohibits rare cooccurrences from disproportionately influencing the resulting embeddings 
The objective ( EQREF6 ) is minimized using stochastic gradient descent by iterating over the matrix of cooccurrence records INLINEFORM0   In the GloVe algorithm, for a given word INLINEFORM1 , the final word representation is taken to be the average of the two intermediate vector representations obtained from ( EQREF6 ); i e, INLINEFORM2   In the next section, we detail the enhancements made to ( EQREF6 ) for the purposes of enhanced interpretability, using the aforementioned framework as our basis 
Imparting Interpretability
Our approach falls into a joint-learning framework where the distributional information extracted from the corpus is allowed to fuse with the external lexicon-based information  Word-groups extracted from Roget's Thesaurus are directly mapped to individual dimensions of word embeddings  Specifically, the vector representations of words that belong to a particular group are encouraged to have deliberately increased values in a particular dimension that corresponds to the word-group under consideration  This can be achieved by modifying the objective function of the embedding algorithm to partially influence vector representation distributions across their dimensions over an input vocabulary  To do this, we propose the following modification to the GloVe objective in ( EQREF6 ): rCl J = i,j=1V f(Xij)[ (wiTwj + bi + bj -Xij)2
+ k(l=1D INLINEFORM0 iFl g(wi,l) + l=1D INLINEFORM1 j Fl g(wj,l) ) ]  In ( SECREF4 ), INLINEFORM2 denotes the indices for the elements of the INLINEFORM3 th concept word-group which we wish to assign in the vector dimension INLINEFORM4   The objective ( SECREF4 ) is designed as a mixture of two individual cost terms: the original GloVe cost term along with a second term that encourages embedding vectors of a given concept word-group to achieve deliberately increased values along an associated dimension INLINEFORM5   The relative weight of the second term is controlled by the parameter INLINEFORM6   The simultaneous minimization of both objectives ensures that words that are similar to, but not included in, one of these concept word-groups are also “nudged” towards the associated dimension INLINEFORM7   The trained word vectors are thus encouraged to form a distribution where the individual vector dimensions align with certain semantic concepts represented by a collection of concept word-groups, one assigned to each vector dimension  To facilitate this behaviour, ( SECREF4 ) introduces a monotone decreasing function INLINEFORM8 defined as INLINEFORM9
which serves to increase the total cost incurred if the value of the INLINEFORM0 th dimension for the two vector representations INLINEFORM1 and INLINEFORM2 for a concept word INLINEFORM3 with INLINEFORM4 fails to be large enough  INLINEFORM5 is also shown in Fig  FIGREF7  
The objective ( SECREF4 ) is minimized using stochastic gradient descent over the cooccurrence records INLINEFORM0   Intuitively, the terms added to ( SECREF4 ) in comparison with ( EQREF6 ) introduce the effect of selectively applying a positive step-type input to the original descent updates of ( EQREF6 ) for concept words along their respective vector dimensions, which influences the dimension value in the positive direction  The parameter INLINEFORM1 in ( SECREF4 ) allows for the adjustment of the magnitude of this influence as needed 
In the next section, we demonstrate the feasibility of this approach by experiments with an example collection of concept word-groups extracted from Roget's Thesaurus 
Experiments and Results
We first identified 300 concepts, one for each dimension of the 300-dimensional vector representation, by employing Roget's Thesaurus  This thesaurus follows a tree structure which starts with a Root node that contains all the words and phrases in the thesaurus  The root node is successively split into Classes and Sections, which are then (optionally) split into Subsections of various depths, finally ending in Categories, which constitute the smallest unit of word/phrase collections in the structure  The actual words and phrases descend from these Categories, and make up the leaves of the tree structure  We note that a given word typically appears in multiple categories corresponding to the different senses of the word  We constructed concept word-groups from Roget's Thesaurus as follows: We first filtered out the multi-word phrases and the relatively obscure terms from the thesaurus  The obscure terms were identified by checking them against a vocabulary extracted from Wikipedia  We then obtained 300 word-groups as the result of a partitioning operation applied to the subtree that ends with categories as its leaves  The partition boundaries, hence the resulting word-groups, can be chosen in many different ways  In our proposed approach, we have chosen to determine this partitioning by traversing this tree structure from the root node in breadth-first order, and by employing a parameter INLINEFORM0 for the maximum size of a node  Here, the size of a node is defined as the number of unique words that ever-descend from that node  During the traversal, if the size of a given node is less than this threshold, we designate the words that ultimately descend from that node as a concept word-group  Otherwise, if the node has children, we discard the node, and queue up all its children for further consideration  If this node does not have any children, on the other hand, the node is truncated to INLINEFORM1 elements with the highest frequency-ranks, and the resulting words are designated as a concept word-group  We note that the choice of INLINEFORM2 greatly affects the resulting collection of word-groups: Excessively large values result in few word-groups that greatly overlap with one another, while overly small values result in numerous tiny word-groups that fail to adequately represent a concept  We experimentally determined that a INLINEFORM3 value of 452 results in the most healthy number of relatively large word-groups (113 groups with size INLINEFORM4 100), while yielding a preferably small overlap amongst the resulting word-groups (with average overlap size not exceeding 3 words)  A total of 566 word-groups were thus obtained  259 smallest word-groups (with size INLINEFORM5 38) were discarded to bring down the number of word-groups to 307  Out of these, 7 groups with the lowest median frequency-rank were further discarded, which yields the final 300 concept word-groups used in the experiments  We present some of the resulting word-groups in Table TABREF9  
By using the concept word-groups, we have trained the GloVe algorithm with the proposed modification given in Section SECREF4 on a snapshot of English Wikipedia measuring 8GB in size, with the stop-words filtered out  Using the parameters given in Table TABREF10 , this resulted in a vocabulary size of 287,847  For the weighting parameter in Eq  SECREF4 , we used a value of INLINEFORM0   The algorithm was trained over 20 iterations  The GloVe algorithm without any modifications was also trained as a baseline with the same parameters  In addition to the original GloVe algorithm, we compare our proposed method with previous studies that aim to obtain interpretable word vectors  We train the improved projected gradient model proposed in BIBREF20 to obtain word vectors (called OIWE-IPG) using the same corpus we use to train GloVe and our proposed method  Using the methods proposed in BIBREF23 , BIBREF26 , BIBREF24 on our baseline GloVe embeddings, we obtain SOV, SPINE and Parsimax (orthogonal) word representations, respectively  We train all the models with the proposed parameters  However, in BIBREF26 , the authors show results for a relatively small vocabulary of 15,000 words  When we trained their model on our baseline GloVe embeddings with a large vocabulary of size 287,847, the resulting vectors performed significantly poor on word similarity tasks compared to the results presented in their paper  In addition, Parsimax (orthogonal) word vectors obtained using method in BIBREF26 are nearly identical to the baseline vectors (i e  learned orthogonal transformation matrix is very close to identity)  Therefore, Parsimax (orthogonal) yields almost same results with baseline vectors in all evaluations  We evaluate the interpretability of the resulting embeddings qualitatively and quantitatively  We also test the performance of the embeddings on word similarity and word analogy tests 
In our experiments, vocabulary size is close to 300,000 while only 16,242 unique words of the vocabulary are present in the concept groups  Furthermore, only dimensions that correspond to the concept group of the word will be updated due to the additional cost term  Given that these concept words can belong to multiple concept groups (2 on average), only 33,319 parameters are updated  There are 90 million individual parameters present for the 300,000 word vectors of size 300  Of these parameters, only approximately 33,000 are updated by the additional cost term 
Qualitative Evaluation for Interpretability
In Fig  FIGREF13 , we demonstrate the particular way in which the proposed algorithm ( SECREF4 ) influences the vector representation distributions  Specifically, we consider, for illustration, the 32nd dimension values for the original GloVe algorithm and our modified version, restricting the plots to the top-1000 words with respect to their frequency ranks for clarity of presentation  In Fig  FIGREF13 , the words in the horizontal axis are sorted in descending order with respect to the values at the 32nd dimension of their word embedding vectors coming from the original GloVe algorithm  The dimension values are denoted with blue and red/green markers for the original and the proposed algorithms, respectively  Additionally, the top-50 words that achieve the greatest 32nd dimension values among the considered 1000 words are emphasized with enlarged markers, along with text annotations  In the presented simulation of the proposed algorithm, the 32nd dimension values are encoded with the concept JUDGMENT, which is reflected as an increase in the dimension values for words such as committee, academy, and article  We note that these words (red) are not part of the pre-determined word-group for the concept JUDGMENT, in contrast to words such as award, review and account (green) which are  This implies that the increase in the corresponding dimension values seen for these words is attributable to the joint effect of the first term in ( SECREF4 ) which is inherited from the original GloVe algorithm, in conjunction with the remaining terms in the proposed objective expression ( SECREF4 )  This experiment illustrates that the proposed algorithm is able to impart the concept of JUDGMENT on its designated vector dimension above and beyond the supplied list of words belonging to the concept word-group for that dimension  We also present the list of words with the greatest dimension value for the dimensions 11, 13, 16, 31, 36, 39, 41, 43 and 79 in Table TABREF11   These dimensions are aligned/imparted with the concepts that are given in the column headers  In Table TABREF11 , the words that are highlighted with green denote the words that exist in the corresponding word-group obtained from Roget's Thesaurus (and are thus explicitly forced to achieve increased dimension values), while the red words denote the words that achieve increased dimension values by virtue of their cooccurrence statistics with the thesaurus-based words (indirectly, without being explicitly forced)  This again illustrates that a semantic concept can indeed be coded to a vector dimension provided that a sensible lexical resource is used to guide semantically related words to the desired vector dimension via the proposed objective function in ( SECREF4 )  Even the words that do not appear in, but are semantically related to, the word-groups that we formed using Roget's Thesaurus, are indirectly affected by the proposed algorithm  They also reflect the associated concepts at their respective dimensions even though the objective functions for their particular vectors are not modified  This point cannot be overemphasized  Although the word-groups extracted from Roget's Thesaurus impose a degree of supervision to the process, the fact that the remaining words in the entire vocabulary are also indirectly affected makes the proposed method a semi-supervised approach that can handle words that are not in these chosen word-groups  A qualitative example of this result can be seen in the last column of Table TABREF11   It is interesting to note the appearance of words such as guerilla, insurgency, mujahideen, Wehrmacht and Luftwaffe in addition to the more obvious and straightforward army, soldiers and troops, all of which are not present in the associated word-group WARFARE 
Most of the dimensions we investigated exhibit similar behaviour to the ones presented in Table TABREF11   Thus generally speaking, we can say that the entries in Table TABREF11 are representative of the great majority  However, we have also specifically looked for dimensions that make less sense and determined a few such dimensions which are relatively less satisfactory  These less satisfactory examples are given in Table TABREF14   These examples are also interesting in that they shed insight into the limitations posed by polysemy and existence of very rare outlier words 
Quantitative Evaluation for Interpretability
One of the main goals of this study is to improve the interpretability of dense word embeddings by aligning the dimensions with predefined concepts from a suitable lexicon  A quantitative measure is required to reliably evaluate the achieved improvement  One of the methods proposed to measure the interpretability is the word intrusion test BIBREF41   But, this method is expensive to apply since it requires evaluations from multiple human evaluators for each embedding dimension  In this study, we use a semantic category-based approach based on the method and category dataset (SEMCAT) introduced in BIBREF27 to quantify interpretability  Specifically, we apply a modified version of the approach presented in BIBREF40 in order to consider possible sub-groupings within the categories in SEMCAT  Interpretability scores are calculated using Interpretability Score (IS) as given below:
DISPLAYFORM0
In ( EQREF17 ), INLINEFORM0 and INLINEFORM1 represents the interpretability scores in the positive and negative directions of the INLINEFORM2 dimension ( INLINEFORM3 , INLINEFORM4 number of dimensions in the embedding space) of word embedding space for the INLINEFORM5 category ( INLINEFORM6 , INLINEFORM7 is number of categories in SEMCAT, INLINEFORM8 ) in SEMCAT respectively  INLINEFORM9 is the set of words in the INLINEFORM10 category in SEMCAT and INLINEFORM11 is the number of words in INLINEFORM12   INLINEFORM13 corresponds to the minimum number of words required to construct a semantic category (i e  represent a concept)  INLINEFORM14 represents the set of INLINEFORM15 words that have the highest ( INLINEFORM16 ) and lowest ( INLINEFORM17 ) values in INLINEFORM18 dimension of the embedding space  INLINEFORM19 is the intersection operator and INLINEFORM20 is the cardinality operator (number of elements) for the intersecting set  In ( EQREF17 ), INLINEFORM21 gives the interpretability score for the INLINEFORM22 dimension and INLINEFORM23 gives the average interpretability score of the embedding space 
Fig  FIGREF18 presents the measured average interpretability scores across dimensions for original GloVe embeddings, for the proposed method and for the other four methods we compare, along with a randomly generated embedding  Results are calculated for the parameters INLINEFORM0 and INLINEFORM1   Our proposed method significantly improves the interpretability for all INLINEFORM2 compared to the original GloVe approach  Our proposed method is second to only SPINE in increasing interpretability  However, as we will experimentally demonstrate in the next subsection, in doing this, SPINE almost entirely destroys the underlying semantic structure of the word embeddings, which is the primary function of a word embedding 
The proposed method and interpretability measurements are both based on utilizing concepts represented by word-groups  Therefore it is expected that there will be higher interpretability scores for some of the dimensions for which the imparted concepts are also contained in SEMCAT  However, by design, word groups that they use are formed by using different sources and are independent  Interpretability measurements use SEMCAT while our proposed method utilizes Roget's Thesaurus 
Intrinsic Evaluation of the Embeddings
It is necessary to show that the semantic structure of the original embedding has not been damaged or distorted as a result of aligning the dimensions with given concepts, and that there is no substantial sacrifice involved from the performance that can be obtained with the original GloVe  To check this, we evaluate performances of the proposed embeddings on word similarity BIBREF42 and word analogy BIBREF0 tests  We compare the results with the original embeddings and the three alternatives excluding Parsimax BIBREF26 since orthogonal transformations will not affect the performance of the original embeddings on these tests 
Word similarity test measures the correlation between word similarity scores obtained from human evaluation (i e  true similarities) and from word embeddings (usually using cosine similarity)  In other words, this test quantifies how well the embedding space reflects human judgements in terms of similarities between different words  The correlation scores for 13 different similarity test sets are reported in Table TABREF20   We observe that, let alone a reduction in performance, the obtained scores indicate an almost uniform improvement in the correlation values for the proposed algorithm, outperforming all the alternatives in almost all test sets  Categories from Roget's thesaurus are groupings of words that are similar in some sense which the original embedding algorithm may fail to capture  These test results signify that the semantic information injected into the algorithm by the additional cost term is significant enough to result in a measurable improvement  It should also be noted that scores obtained by SPINE is unacceptably low on almost all tests indicating that it has achieved its interpretability performance at the cost of losing its semantic functions 
Word analogy test is introduced in BIBREF1 and looks for the answers of the questions that are in the form of "X is to Y, what Z is to ?" by applying simple arithmetic operations to vectors of words X, Y and Z  We present precision scores for the word analogy tests in Table TABREF21   It can be seen that the alternative approaches that aim to improve interpretability, have poor performance on the word analogy tests  However, our proposed method has comparable performance with the original GloVe embeddings  Our method outperforms GloVe in semantic analogy test set and in overall results, while GloVe performs slightly better in syntactic test set  This comparable performance is mainly due to the cost function of our proposed method that includes the original objective of the GloVe 
To investigate the effect of the additional cost term on the performance improvement in the semantic analogy test, we present Table TABREF22   In particular, we present results for the cases where i) all questions in the dataset are considered, ii) only the questions that contains at least one concept word are considered, iii) only the questions that consist entirely of concept words are considered  We note specifically that for the last case, only a subset of the questions under the semantic category family txt ended up being included  We observe that for all three scenarios, our proposed algorithm results in an improvement in the precision scores  However, the greatest performance increase is seen for the last scenario, which underscores the extent to which the semantic features captured by embeddings can be improved with a reasonable selection of the lexical resource from which the concept word-groups were derived 
Conclusion
We presented a novel approach to impart interpretability into word embeddings  We achieved this by encouraging different dimensions of the vector representation to align with predefined concepts, through the addition of an additional cost term in the optimization objective of the GloVe algorithm that favors a selective increase for a pre-specified input of concept words along each dimension 
We demonstrated the efficacy of this approach by applying qualitative and quantitative evaluations for interpretability  We also showed via standard word-analogy and word-similarity tests that the semantic coherence of the original vector space is preserved, even slightly improved  We have also performed and reported quantitative comparisons with several other methods for both interpretabilty increase and preservation of semantic coherence  Upon inspection of Fig  FIGREF18 and Tables TABREF20 , TABREF21 , and TABREF22 altogether, it should be noted that our proposed method achieves both of the objectives simultaneously, increased interpretability and preservation of the intrinsic semantic structure 
An important point was that, while it is expected for words that are already included in the concept word-groups to be aligned together since their dimensions are directly updated with the proposed cost term, it was also observed that words not in these groups also aligned in a meaningful manner without any direct modification to their cost function  This indicates that the cost term we added works productively with the original cost function of GloVe to handle words that are not included in the original concept word-groups, but are semantically related to those word-groups  The underlying mechanism can be explained as follows  While the outside lexical resource we introduce contains a relatively small number of words compared to the total number of words, these words and the categories they represent have been carefully chosen and in a sense, "densely span" all the words in the language  By saying "span", we mean they cover most of the concepts and ideas in the language without leaving too many uncovered areas  With "densely" we mean all areas are covered with sufficient strength  In other words, this subset of words is able to constitute a sufficiently strong skeleton, or scaffold  Now remember that GloVe works to align or bring closer related groups of words, which will include words from the lexical source  So the joint action of aligning the words with the predefined categories (introduced by us) and aligning related words (handled by GloVe) allows words not in the lexical groups to also be aligned meaningfully  We may say that the non-included words are "pulled along" with the included words by virtue of the "strings" or "glue" that is provided by GloVe  In numbers, the desired effect is achieved by manipulating less than only 0 05% of parameters of the entire word vectors  Thus, while there is a degree of supervision coming from the external lexical resource, the rest of the vocabulary is also aligned indirectly in an unsupervised way  This may be the reason why, unlike earlier proposed approaches, our method is able to achieve increasing interpretability without destroying underlying semantic structure, and consequently without sacrificing performance in benchmark tests 
Upon inspecting the 2nd column of Table TABREF14 , where qualitative results for concept TASTE are presented, another insight regarding the learning mechanism of our proposed approach can be made  Here it seems understandable that our proposed approach, along with GloVe, brought together the words taste and polish, and then the words Polish and, for instance, Warsaw are brought together by GloVe  These examples are interesting in that they shed insight into how GloVe works and the limitations posed by polysemy  It should be underlined that the present approach is not totally incapable of handling polysemy, but cannot do so perfectly  Since related words are being clustered, sufficiently well-connected words that do not meaningfully belong along with others will be appropriately "pulled away" from that group by several words, against the less effective, inappropriate pull of a particular word  Even though polish with lowercase "p" belongs where it is, it is attracting Warsaw to itself through polysemy and this is not meaningful  Perhaps because Warsaw is not a sufficiently well-connected word, it ends being dragged along, although words with greater connectedness to a concept group might have better resisted such inappropriate attractions 
In this study, we used the GloVe algorithm as the underlying dense word embedding scheme to demonstrate our approach  However, we stress that it is possible for our approach to be extended to other word embedding algorithms which have a learning routine consisting of iterations over cooccurrence records, by making suitable adjustments in the objective function  Since word2vec model is also based on the coocurrences of words in a sliding window through a large corpus, we expect that our approach can also be applied to word2vec after making suitable adjustments, which can be considered as an immediate future work for our approach  Although the semantic concepts are encoded in only one direction (positive) within the embedding dimensions, it might be beneficial to pursue future work that also encodes opposite concepts, such as good and bad, in two opposite directions of the same dimension 
The proposed methodology can also be helpful in computational cross-lingual studies, where the similarities are explored across the vector spaces of different languages BIBREF43 , BIBREF44  
Context 2: Introduction
Data imbalance is a common issue in a variety of NLP tasks such as tagging and machine reading comprehension  Table TABREF3 gives concrete examples: for the Named Entity Recognition (NER) task BIBREF2, BIBREF3, most tokens are backgrounds with tagging class $O$  Specifically, the number of tokens tagging class $O$ is 5 times as many as those with entity labels for the CoNLL03 dataset and 8 times for the OntoNotes5 0 dataset; Data-imbalanced issue is more severe for MRC tasks BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8 with the value of negative-positive ratio being 50-200 
Data imbalance results in the following two issues: (1) the training-test discrepancy: Without balancing the labels, the learning process tends to converge to a point that strongly biases towards class with the majority label  This actually creates a discrepancy between training and test: at training time, each training instance contributes equally to the objective function while at test time, F1 score concerns more about positive examples; (2) the overwhelming effect of easy-negative examples  As pointed out by meng2019dsreg, significantly large number of negative examples also means that the number of easy-negative example is large  The huge number of easy examples tends to overwhelm the training, making the model not sufficiently learned to distinguish between positive examples and hard-negative examples  The cross-entropy objective (CE for short) or maximum likelihood (MLE) objective, which is widely adopted as the training objective for data-imbalanced NLP tasks BIBREF9, BIBREF10, BIBREF11, BIBREF12, BIBREF13, BIBREF14, BIBREF15, handles neither of the issues 
To handle the first issue, we propose to replace CE or MLE with losses based on the Sørensen–Dice coefficient BIBREF0 or Tversky index BIBREF1  The Sørensen–Dice coefficient, dice loss for short, is the harmonic mean of precision and recall  It attaches equal importance to false positives (FPs) and false negatives (FNs) and is thus more immune to data-imbalanced datasets  Tversky index extends dice loss by using a weight that trades precision and recall, which can be thought as the approximation of the $F_{\beta }$ score, and thus comes with more flexibility  Therefore, We use dice loss or Tversky index to replace CE loss to address the first issue 
Only using dice loss or Tversky index is not enough since they are unable to address the dominating influence of easy-negative examples  This is intrinsically because dice loss is actually a hard version of the F1 score  Taking the binary classification task as an example, at test time, an example will be classified as negative as long as its probability is smaller than 0 5, but training will push the value to 0 as much as possible  This gap isn't a big issue for balanced datasets, but is extremely detrimental if a big proportion of training examples are easy-negative ones: easy-negative examples can easily dominate training since their probabilities can be pushed to 0 fairly easily  Meanwhile, the model can hardly distinguish between hard-negative examples and positive ones  Inspired by the idea of focal loss BIBREF16 in computer vision, we propose a dynamic weight adjusting strategy, which associates each training example with a weight in proportion to $(1-p)$, and this weight dynamically changes as training proceeds  This strategy helps to deemphasize confident examples during training as their $p$ approaches the value of 1, makes the model attentive to hard-negative examples, and thus alleviates the dominating effect of easy-negative examples 
Combing both strategies, we observe significant performance boosts on a wide range of data imbalanced NLP tasks  Notably, we are able to achieve SOTA results on CTB5 (97 92, +1 86), CTB6 (96 57, +1 80) and UD1 4 (96 98, +2 19) for the POS task; SOTA results on CoNLL03 (93 33, +0 29), OntoNotes5 0 (92 07, +0 96)), MSRA 96 72(+0 97) and OntoNotes4 0 (84 47,+2 36) for the NER task; along with competitive results on the tasks of machine reading comprehension and paraphrase identification 
The rest of this paper is organized as follows: related work is presented in Section 2  We describe different training objectives in Section 3  Experimental results are presented in Section 4  We perform ablation studies in Section 5, followed by a brief conclusion in Section 6 
Related Work ::: Data Resample
The idea of weighting training examples has a long history  Importance sampling BIBREF17 assigns weights to different samples and changes the data distribution  Boosting algorithms such as AdaBoost BIBREF18 select harder examples to train subsequent classifiers  Similarly, hard example mining BIBREF19 downsamples the majority class and exploits the most difficult examples  Oversampling BIBREF20, BIBREF21 is used to balance the data distribution  Another line of data resampling is to dynamically control the weights of examples as training proceeds  For example, focal loss BIBREF16 used a soft weighting scheme that emphasizes harder examples during training  In self-paced learning BIBREF22, example weights are obtained through optimizing the weighted training loss which encourages learning easier examples first  At each training step, self-paced learning algorithm optimizes model parameters and example weights jointly  Other works BIBREF23, BIBREF24 adjusted the weights of different training examples based on training loss  Besides, recent work BIBREF25, BIBREF26 proposed to learn a separate network to predict sample weights 
Related Work ::: Data Imbalance Issue in Object Detection
The background-object label imbalance issue is severe and thus well studied in the field of object detection BIBREF27, BIBREF28, BIBREF29, BIBREF30, BIBREF31  The idea of hard negative mining (HNM) BIBREF30 has gained much attention recently  shrivastava2016ohem proposed the online hard example mining (OHEM) algorithm in an iterative manner that makes training progressively more difficult, and pushes the model to learn better  ssd2016liu sorted all of the negative samples based on the confidence loss and picking the training examples with the negative-positive ratio at 3:1  pang2019rcnn proposed a novel method called IoU-balanced sampling and aploss2019chen designed a ranking model to replace the conventional classification task with a average-precision loss to alleviate the class imbalance issue  The efforts made on object detection have greatly inspired us to solve the data imbalance issue in NLP 
Losses ::: Notation
For illustration purposes, we use the binary classification task to demonstrate how different losses work  The mechanism can be easily extended to multi-class classification 
Let $\lbrace x_i\rbrace $ denote a set of instances  Each $x_i$ is associated with a golden label vector $y_i = [y_{i0},y_{i1} ]$, where $y_{i1}\in \lbrace 0,1\rbrace $ and $y_{i0}\in \lbrace 0,1\rbrace $ respectively denote the positive and negative classes, and thus $y_i$ can be either $[0,1]$ or $[0,1]$  Let $p_i = [p_{i0},p_{i1} ]$ denote the probability vector, and $p_{i1}$ and $p_{i0}$ respectively denote the probability that a model assigns the positive and negative label to $x_i$ 
Losses ::: Cross Entropy Loss
The vanilla cross entropy (CE) loss is given by:
As can be seen from Eq DISPLAY_FORM8, each $x_i$ contributes equally to the final objective  Two strategies are normally used to address the the case where we wish that not all $x_i$ are treated equal: associating different classes with different weighting factor $\alpha $ or resampling the datasets  For the former, Eq DISPLAY_FORM8 is adjusted as follows:
where $\alpha _i\in [0,1]$ may be set by the inverse class frequency or treated as a hyperparameter to set by cross validation  In this work, we use $\lg (\frac{n-n_t}{n_t}+K)$ to calculate the coefficient $\alpha $, where $n_t$ is the number of samples with class $t$ and $n$ is the total number of samples in the training set  $K$ is a hyperparameter to tune  The data resampling strategy constructs a new dataset by sampling training examples from the original dataset based on human-designed criteria, e g , extract equal training samples from each class  Both strategies are equivalent to changing the data distribution and thus are of the same nature  Empirically, these two methods are not widely used due to the trickiness of selecting $\alpha $ especially for multi-class classification tasks and that inappropriate selection can easily bias towards rare classes BIBREF32 
Losses ::: Dice coefficient and Tversky index
Sørensen–Dice coefficient BIBREF0, BIBREF33, dice coefficient (DSC) for short, is a F1-oriented statistic used to gauge the similarity of two sets  Given two sets $A$ and $B$, the dice coefficient between them is given as follows:
In our case, $A$ is the set that contains of all positive examples predicted by a specific model, and $B$ is the set of all golden positive examples in the dataset  When applied to boolean data with the definition of true positive (TP), false positive (FP), and false negative (FN), it can be then written as follows:
For an individual example $x_i$, its corresponding DSC loss is given as follows:
As can be seen, for a negative example with $y_{i1}=0$, it does not contribute to the objective  For smoothing purposes, it is common to add a $\gamma $ factor to both the nominator and the denominator, making the form to be as follows:
As can be seen, negative examples, with $y_{i1}$ being 0 and DSC being $\frac{\gamma }{ p_{i1}+\gamma }$, also contribute to the training  Additionally, milletari2016v proposed to change the denominator to the square form for faster convergence, which leads to the following dice loss (DL):
Another version of DL is to directly compute set-level dice coefficient instead of the sum of individual dice coefficient  We choose the latter due to ease of optimization 
Tversky index (TI), which can be thought as the approximation of the $F_{\beta }$ score, extends dice coefficient to a more general case  Given two sets $A$ and $B$, tversky index is computed as follows:
Tversky index offers the flexibility in controlling the tradeoff between false-negatives and false-positives  It degenerates to DSC if $\alpha =\beta =0 5$  The Tversky loss (TL) for the training set $\lbrace x_i,y_i\rbrace $ is thus as follows:
Losses ::: Self-adusting Dice Loss
Consider a simple case where the dataset consists of only one example $x_i$, which is classified as positive as long as $p_{i1}$ is larger than 0 5  The computation of $F1$ score is actually as follows:
Comparing Eq DISPLAY_FORM14 with Eq DISPLAY_FORM22, we can see that Eq DISPLAY_FORM14 is actually a soft form of $F1$, using a continuous $p$ rather than the binary $\mathbb {I}( p_{i1}>0 5)$  This gap isn't a big issue for balanced datasets, but is extremely detrimental if a big proportion of training examples are easy-negative ones: easy-negative examples can easily dominate training since their probabilities can be pushed to 0 fairly easily  Meanwhile, the model can hardly distinguish between hard-negative examples and positive ones, which has a huge negative effect on the final F1 performance 
To address this issue, we propose to multiply the soft probability $p$ with a decaying factor $(1-p)$, changing Eq DISPLAY_FORM22 to the following form:
One can think $(1-p_{i1})$ as a weight associated with each example, which changes as training proceeds  The intuition of changing $p_{i1}$ to $(1-p_{i1}) p_{i1}$ is to push down the weight of easy examples  For easy examples whose probability are approaching 0 or 1, $(1-p_{i1}) p_{i1}$ makes the model attach significantly less focus to them  Figure FIGREF23 gives gives an explanation from the perspective in derivative: the derivative of $\frac{(1-p)p}{1+(1-p)p}$ with respect to $p$ approaches 0 immediately after $p$ approaches 0, which means the model attends less to examples once they are correctly classified 
A close look at Eq DISPLAY_FORM14 reveals that it actually mimics the idea of focal loss (FL for short) BIBREF16 for object detection in vision  Focal loss was proposed for one-stage object detector to handle foreground-background tradeoff encountered during training  It down-weights the loss assigned to well-classified examples by adding a $(1-p)^{\beta }$ factor, leading the final loss to be $(1-p)^{\beta }\log p$ 
In Table TABREF18, we show the losses used in our experiments, which is described in the next section 
Experiments
We evaluate the proposed method on four NLP tasks: part-of-speech tagging, named entity recognition, machine reading comprehension and paraphrase identification  Baselines in our experiments are optimized by using the standard cross-entropy training objective 
Experiments ::: Part-of-Speech Tagging
Part-of-speech tagging (POS) is the task of assigning a label (e g , noun, verb, adjective) to each word in a given text  In this paper, we choose BERT as the backbone and conduct experiments on three Chinese POS datasets  We report the span-level micro-averaged precision, recall and F1 for evaluation  Hyperparameters are tuned on the corresponding development set of each dataset 
Experiments ::: Part-of-Speech Tagging ::: Datasets
We conduct experiments on the widely used Chinese Treebank 5 0, 6 0 as well as UD1 4 
CTB5 is a Chinese dataset for tagging and parsing, which contains 507,222 words, 824,983 characters and 18,782 sentences extracted from newswire sources 
CTB6 is an extension of CTB5, containing 781,351 words, 1,285,149 characters and 28,295 sentences 
UD is the abbreviation of Universal Dependencies, which is a framework for consistent annotation of grammar (parts of speech, morphological features, and syntactic dependencies) across different human languages  In this work, we use UD1 4 for Chinese POS tagging 
Experiments ::: Part-of-Speech Tagging ::: Baselines
We use the following baselines:
Joint-POS: shao2017character jointly learns Chinese word segmentation and POS 
Lattice-LSTM: lattice2018zhang constructs a word-character lattice 
Bert-Tagger: devlin2018bert treats part-of-speech as a tagging task 
Experiments ::: Part-of-Speech Tagging ::: Results
Table presents the experimental results on the POS task  As can be seen, the proposed DSC loss outperforms the best baseline results by a large margin, i e , outperforming BERT-tagger by +1 86 in terms of F1 score on CTB5, +1 80 on CTB6 and +2 19 on UD1 4  As far as we are concerned, we are achieving SOTA performances on the three datasets  Weighted cross entropy and focal loss only gain a little performance improvement on CTB5 and CTB6, and the dice loss obtains huge gain on CTB5 but not on CTB6, which indicates the three losses are not consistently robust in resolving the data imbalance issue  The proposed DSC loss performs robustly on all the three datasets 
Experiments ::: Named Entity Recognition
Named entity recognition (NER) refers to the task of detecting the span and semantic category of entities from a chunk of text  Our implementation uses the current state-of-the-art BERT-MRC model proposed by xiaoya2019ner as a backbone  For English datasets, we use BERT$_\text{Large}$ English checkpoints, while for Chinese we use the official Chinese checkpoints  We report span-level micro-averaged precision, recall and F1-score  Hyperparameters are tuned on the development set of each dataset 
Experiments ::: Named Entity Recognition ::: Datasets
For the NER task, we consider both Chinese datasets, i e , OntoNotes4 0 BIBREF34 and MSRA BIBREF35, and English datasets, i e , CoNLL2003 BIBREF36 and OntoNotes5 0 BIBREF37 
CoNLL2003 is an English dataset with 4 entity types: Location, Organization, Person and Miscellaneous  We followed data processing protocols in BIBREF14 
English OntoNotes5 0 consists of texts from a wide variety of sources and contains 18 entity types  We use the standard train/dev/test split of CoNLL2012 shared task 
Chinese MSRA performs as a Chinese benchmark dataset containing 3 entity types  Data in MSRA is collected from news domain  Since the development set is not provided in the original MSRA dataset, we randomly split the training set into training and development splits by 9:1  We use the official test set for evaluation 
Chinese OntoNotes4 0 is a Chinese dataset and consists of texts from news domain, which has 18 entity types  In this paper, we take the same data split as wu2019glyce did 
Experiments ::: Named Entity Recognition ::: Baselines
We use the following baselines:
ELMo: a tagging model from peters2018deep 
Lattice-LSTM: lattice2018zhang constructs a word-character lattice, only used in Chinese datasets 
CVT: from kevin2018cross, which uses Cross-View Training(CVT) to improve the representations of a Bi-LSTM encoder 
Bert-Tagger: devlin2018bert treats NER as a tagging task 
Glyce-BERT: wu2019glyce combines glyph information with BERT pretraining 
BERT-MRC: The current SOTA model for both Chinese and English NER datasets proposed by xiaoya2019ner, which formulate NER as machine reading comprehension task 
Experiments ::: Named Entity Recognition ::: Results
Table shows experimental results on NER datasets  For English datasets including CoNLL2003 and OntoNotes5 0, our proposed method outperforms BERT-MRCBIBREF38 by +0 29 and +0 96 respectively  We observe huge performance boosts on Chinese datasets, achieving F1 improvements by +0 97 and +2 36 on MSRA and OntoNotes4 0, respectively  As far as we are concerned, we are setting new SOTA performances on all of the four NER datasets 
Experiments ::: Machine Reading Comprehension
Machine reading comprehension (MRC) BIBREF39, BIBREF40, BIBREF41, BIBREF40, BIBREF42, BIBREF15 has become a central task in natural language understanding  MRC in the SQuAD-style is to predict the answer span in the passage given a question and the passage  In this paper, we choose the SQuAD-style MRC task and report Extract Match (EM) in addition to F1 score on validation set  All hyperparameters are tuned on the development set of each dataset 
Experiments ::: Machine Reading Comprehension ::: Datasets
The following five datasets are used for MRC task: SQuAD v1 1, SQuAD v2 0 BIBREF4, BIBREF6 and Quoref BIBREF8 
SQuAD v1 1 and SQuAD v2 0 are the most widely used QA benchmarks  SQuAD1 1 is a collection of 100K crowdsourced question-answer pairs, and SQuAD2 0 extends SQuAD1 1 allowing no short answer exists in the provided passage 
Quoref is a QA dataset which tests the coreferential reasoning capability of reading comprehension systems, containing 24K questions over 4 7K paragraphs from Wikipedia 
Experiments ::: Machine Reading Comprehension ::: Baselines
We use the following baselines:
QANet: qanet2018 builds a model based on convolutions and self-attention  Convolution to model local interactions and self-attention to model global interactions 
BERT: devlin2018bert treats NER as a tagging task 
XLNet: xlnet2019 proposes a generalized autoregressive pretraining method that enables learning bidirectional contexts 
Experiments ::: Machine Reading Comprehension ::: Results
Table shows the experimental results for MRC tasks  With either BERT or XLNet, our proposed DSC loss obtains significant performance boost on both EM and F1  For SQuADv1 1, our proposed method outperforms XLNet by +1 25 in terms of F1 score and +0 84 in terms of EM and achieves 87 65 on EM and 89 51 on F1 for SQuAD v2 0  Moreover, on QuoRef, the proposed method surpasses XLNet results by +1 46 on EM and +1 41 on F1  Another observation is that, XLNet outperforms BERT by a huge margin, and the proposed DSC loss can obtain further performance improvement by an average score above 1 0 in terms of both EM and F1, which indicates the DSC loss is complementary to the model structures 
Experiments ::: Paraphrase Identification
Paraphrases are textual expressions that have the same semantic meaning using different surface words  Paraphrase identification (PI) is the task of identifying whether two sentences have the same meaning or not  We use BERT BIBREF11 and XLNet BIBREF43 as backbones and report F1 score for comparison  Hyperparameters are tuned on the development set of each dataset 
Experiments ::: Paraphrase Identification ::: Datasets
We conduct experiments on two widely used datasets for PI task: MRPC BIBREF44 and QQP 
MRPC is a corpus of sentence pairs automatically extracted from online news sources, with human annotations of whether the sentence pairs are semantically equivalent  The MRPC dataset has imbalanced classes (68% positive, 32% for negative) 
QQP is a collection of question pairs from the community question-answering website Quora  The class distribution in QQP is also unbalanced (37% positive, 63% negative) 
Experiments ::: Paraphrase Identification ::: Results
Table shows the results for PI task  We find that replacing the training objective with DSC introduces performance boost for both BERT and XLNet  Using DSC loss improves the F1 score by +0 58 for MRPC and +0 73 for QQP 
Ablation Studies ::: The Effect of Dice Loss on Accuracy-oriented Tasks
We argue that the most commonly used cross-entropy objective is actually accuracy-oriented, whereas the proposed dice loss (DL) performs as a hard version of F1-score  To explore the effect of the dice loss on accuracy-oriented tasks such as text classification, we conduct experiments on the Stanford Sentiment Treebank sentiment classification datasets including SST-2 and SST-5  We fine-tune BERT$_\text{Large}$ with different training objectives  Experiment results for SST are shown in   For SST-5, BERT with CE achieves 55 57 in terms of accuracy, with DL and DSC losses slightly degrade the accuracy performance and achieve 54 63 and 55 19, respectively  For SST-2, BERT with CE achieves 94 9 in terms of accuracy  The same as SST-5, we observe a slight performance drop with DL and DSC, which means that the dice loss actually works well for F1 but not for accuracy 
Ablation Studies ::: The Effect of Hyperparameters in Tversky index
As mentioned in Section SECREF10, Tversky index (TI) offers the flexibility in controlling the tradeoff between false-negatives and false-positives  In this subsection, we explore the effect of hyperparameters (i e , $\alpha $ and $\beta $) in TI to test how they manipulate the tradeoff  We conduct experiments on the Chinese OntoNotes4 0 NER dataset and English QuoRef MRC dataset to examine the influence of tradeoff between precision and recall  Experiment results are shown in Table   The highest F1 for Chinese OntoNotes4 0 is 84 67 when $\alpha $ is set to 0 6 while for QuoRef, the highest F1 is 68 44 when $\alpha $ is set to 0 4  In addition, we can observe that the performance varies a lot as $\alpha $ changes in distinct datasets, which shows that the hyperparameters $\alpha ,\beta $ play an important role in the proposed method 
Conclusion
In this paper, we alleviate the severe data imbalance issue in NLP tasks  We propose to use dice loss in replacement of the standard cross-entropy loss, which performs as a soft version of F1 score  Using dice loss can help narrow the gap between training objectives and evaluation metrics  Empirically, we show that the proposed training objective leads to significant performance boost for part-of-speech, named entity recognition, machine reading comprehension and paraphrase identification tasks 
Context 3: 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

ground_truth: Yes

Unanswerable

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 method's improvements of F1 for NER task for English and Chinese datasets?
Context 3:  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) 
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

ground_truth: English datasets including CoNLL2003 and OntoNotes5.0, our proposed method outperforms BERT-MRCBIBREF38 by +0.29 and +0.96 respectively, Chinese datasets, achieving F1 improvements by +0.97 and +2.36 on MSRA and OntoNotes4.0, respectively

For English datasets including CoNLL2003 and OntoNotes5.0, our proposed method outperforms BERT-MRCBIBREF38 by +0.29 and +0.96 respectively., huge performance boosts on Chinese datasets, achieving F1 improvements by +0.97 and +2.36 on MSRA and OntoNotes4.0, respectively

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 3:  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