ymoslem/acl-abstracts huggingface dataset:
On the Role of Corpus Ordering in Language Modeling (2021)

Language models pretrained on vast corpora of unstructured text using self-supervised learning framework are used in numerous natural language understanding and generation tasks. Many studies show that language acquisition in humans follows a rather structured simple-to-complex pattern and guided by this intuition, curriculum learning, which enables training of computational models in a meaningful order, such as processing easy samples before hard ones, has been shown to potentially reduce training time. The question remains whether curriculum learning can benefit pretraining of language models. In this work, we perform comprehensive experiments involving multiple curricula strategies varying the criteria for complexity and the training schedules. Empirical results of training transformer language models on English corpus and evaluating it intrinsically as well as after fine-tuning across eight tasks from the GLUE benchmark, show consistent improvement gains over conventional vanilla training. Interestingly, in our experiments, when evaluated on one epoch, the best model following a document-level hard-to-easy curriculum, outperforms the vanilla model by 1.7 points (average GLUE score) and it takes the vanilla model twice as many training steps to reach comparable performance.
Efficient Domain Adaptation of Language Models via Adaptive Tokenization (2021)

Contextual embedding-based language models trained on large data sets, such as BERT and RoBERTa, provide strong performance across a wide range of tasks and are ubiquitous in modern NLP. It has been observed that fine-tuning these models on tasks involving data from domains different from that on which they were pretrained can lead to suboptimal performance. Recent work has explored approaches to adapt pretrained language models to new domains by incorporating additional pretraining on domain-specific corpora and task data. We propose an alternative approach for transferring pretrained language models to new domains by adapting their tokenizers. We show that domain-specific subword sequences can be determined efficiently directly from divergences in the conditional token distributions of the base and domain-specific corpora. In datasets from four disparate domains, we find adaptive tokenization on a pretrained RoBERTa model provides greater than 85{\%} of the performance benefits of domain specific pretraining. Our approach produces smaller models and less training and inference time than other approaches using tokenizer augmentation. Although using adaptive tokenization incurs a 6{\%} increase in model parameters (due to the introduction of 10k new domain-specific tokens), our approach, using 64 CPUs, is {\ensuremath{>}}72x faster than further pretraining the language model on domain-specific corpora on 8 TPUs.
Unsupervised Contextualized Document Representation (2021)

Several NLP tasks need the effective repre-sentation of text documents.Arora et al.,2017 demonstrate that simple weighted aver-aging of word vectors frequently outperformsneural models. SCDV (Mekala et al., 2017)further extends this from sentences to docu-ments by employing soft and sparse cluster-ing over pre-computed word vectors. How-ever, both techniques ignore the polysemyand contextual character of words. In thispaper, we address this issue by proposingSCDV+BERT(ctxd), a simple and effective un-supervised representation that combines con-textualized BERT (Devlin et al., 2019) basedword embedding for word sense disambigua-tion with SCDV soft clustering approach. Weshow that our embeddings outperform origi-nal SCDV, pre-train BERT, and several otherbaselines on many classification datasets. Wealso demonstrate our embeddings effective-ness on other tasks, such as concept match-ing and sentence similarity. In addition,we show that SCDV+BERT(ctxd) outperformsfine-tune BERT and different embedding ap-proaches in scenarios with limited data andonly few shots examples.
Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledge (2021)

Prior research has explored the ability of computational models to predict a word semantic fit with a given predicate. While much work has been devoted to modeling the typicality relation between verbs and arguments in isolation, in this paper we take a broader perspective by assessing whether and to what extent computational approaches have access to the information about the typicality of entire events and situations described in language (Generalized Event Knowledge). Given the recent success of Transformers Language Models (TLMs), we decided to test them on a benchmark for the dynamic estimation of thematic fit. The evaluation of these models was performed in comparison with SDM, a framework specifically designed to integrate events in sentence meaning representations, and we conducted a detailed error analysis to investigate which factors affect their behavior. Our results show that TLMs can reach performances that are comparable to those achieved by SDM. However, additional analysis consistently suggests that TLMs do not capture important aspects of event knowledge, and their predictions often depend on surface linguistic features, such as frequent words, collocations and syntactic patterns, thereby showing sub-optimal generalization abilities.
Can Transformer Language Models Predict Psychometric Properties? (2021)

Transformer-based language models (LMs) continue to advance state-of-the-art performance on NLP benchmark tasks, including tasks designed to mimic human-inspired {\textquotedblleft}commonsense{\textquotedblright} competencies. To better understand the degree to which LMs can be said to have certain linguistic reasoning skills, researchers are beginning to adapt the tools and concepts of the field of psychometrics. But to what extent can the benefits flow in the other direction? I.e., can LMs be of use in predicting what the psychometric properties of test items will be when those items are given to human participants? We gather responses from numerous human participants and LMs (transformer- and non-transformer-based) on a broad diagnostic test of linguistic competencies. We then use the responses to calculate standard psychometric properties of the items in the diagnostic test, using the human responses and the LM responses separately. We then determine how well these two sets of predictions match. We find cases in which transformer-based LMs predict psychometric properties consistently well in certain categories but consistently poorly in others, thus providing new insights into fundamental similarities and differences between human and LM reasoning.
Semantic shift in social networks (2021)

Just as the meaning of words is tied to the communities in which they are used, so too is semantic change. But how does lexical semantic change manifest differently across different communities? In this work, we investigate the relationship between community structure and semantic change in 45 communities from the social media website Reddit. We use distributional methods to quantify lexical semantic change and induce a social network on communities, based on interactions between members. We explore the relationship between semantic change and the clustering coefficient of a community`s social network graph, as well as community size and stability. While none of these factors are found to be significant on their own, we report a significant effect of their three-way interaction. We also report on significant word-level effects of frequency and change in frequency, which replicate previous findings.
A Study on Using Semantic Word Associations to Predict the Success of a Novel (2021)

Many new books get published every year, and only a fraction of them become popular among the readers. So the prediction of a book success can be a very useful parameter for publishers to make a reliable decision. This article presents the study of semantic word associations using the word embedding of book content for a set of Roget`s thesaurus concepts for book success prediction. In this work, we discuss the method to represent a book as a spectrum of concepts based on the association score between its content embedding and a global embedding (i.e. fastText) for a set of semantically linked word clusters. We show that the semantic word associations outperform the previous methods for book success prediction. In addition, we present that semantic word associations also provide better results than using features like the frequency of word groups in Roget`s thesaurus, LIWC (a popular tool for linguistic inquiry and word count), NRC (word association emotion lexicon), and part of speech (PoS). Our study reports that concept associations based on Roget`s Thesaurus using word embedding of individual novel resulted in the state-of-the-art performance of 0.89 average weighted F1-score for book success prediction. Finally, we present a set of dominant themes that contribute towards the popularity of a book for a specific genre.
Recovering Lexically and Semantically Reused Texts (2021)

Writers often repurpose material from existing texts when composing new documents. Because most documents have more than one source, we cannot trace these connections using only models of document-level similarity. Instead, this paper considers methods for local text reuse detection (LTRD), detecting localized regions of lexically or semantically similar text embedded in otherwise unrelated material. In extensive experiments, we study the relative performance of four classes of neural and bag-of-words models on three LTRD tasks {--} detecting plagiarism, modeling journalists' use of press releases, and identifying scientists' citation of earlier papers. We conduct evaluations on three existing datasets and a new, publicly-available citation localization dataset. Our findings shed light on a number of previously-unexplored questions in the study of LTRD, including the importance of incorporating document-level context for predictions, the applicability of of-the-shelf neural models pretrained on {\textquotedblleft}general{\textquotedblright} semantic textual similarity tasks such as paraphrase detection, and the trade-offs between more efficient bag-of-words and feature-based neural models and slower pairwise neural models.
Generating Hypothetical Events for Abductive Inference (2021)

Abductive reasoning starts from some observations and aims at finding the most plausible explanation for these observations. To perform abduction, humans often make use of temporal and causal inferences, and knowledge about how some hypothetical situation can result in different outcomes. This work offers the first study of how such knowledge impacts the Abductive NLI task {--} which consists in choosing the more likely explanation for given observations. We train a specialized language model LMI that is tasked to generate what could happen next from a hypothetical scenario that evolves from a given event. We then propose a multi-task model MTL to solve the Abductive NLI task, which predicts a plausible explanation by a) considering different possible events emerging from candidate hypotheses {--} events generated by LMI {--} and b) selecting the one that is most similar to the observed outcome. We show that our MTL model improves over prior vanilla pre-trained LMs fine-tuned on Abductive NLI. Our manual evaluation and analysis suggest that learning about possible next events from different hypothetical scenarios supports abductive inference.
NeuralLog: Natural Language Inference with Joint Neural and Logical Reasoning (2021)

Deep learning (DL) based language models achieve high performance on various benchmarks for Natural Language Inference (NLI). And at this time, symbolic approaches to NLI are receiving less attention. Both approaches (symbolic and DL) have their advantages and weaknesses. However, currently, no method combines them in a system to solve the task of NLI. To merge symbolic and deep learning methods, we propose an inference framework called NeuralLog, which utilizes both a monotonicity-based logical inference engine and a neural network language model for phrase alignment. Our framework models the NLI task as a classic search problem and uses the beam search algorithm to search for optimal inference paths. Experiments show that our joint logic and neural inference system improves accuracy on the NLI task and can achieve state-of-art accuracy on the SICK and MED datasets.
Teach the Rules, Provide the Facts: Targeted Relational-knowledge Enhancement for Textual Inference (2021)

We present InferBert, a method to enhance transformer-based inference models with relevant relational knowledge. Our approach facilitates learning generic inference patterns requiring relational knowledge (e.g. inferences related to hypernymy) during training, while injecting on-demand the relevant relational facts (e.g. pangolin is an animal) at test time. We apply InferBERT to the NLI task over a diverse set of inference types (hypernymy, location, color, and country of origin), for which we collected challenge datasets. In this setting, InferBert succeeds to learn general inference patterns, from a relatively small number of training instances, while not hurting performance on the original NLI data and substantially outperforming prior knowledge enhancement models on the challenge data. It further applies its inferences successfully at test time to previously unobserved entities. InferBert is computationally more efficient than most prior methods, in terms of number of parameters, memory consumption and training time.
ParsFEVER: a Dataset for Farsi Fact Extraction and Verification (2021)

Training and evaluation of automatic fact extraction and verification techniques require large amounts of annotated data which might not be available for low-resource languages. This paper presents ParsFEVER: the first publicly available Farsi dataset for fact extraction and verification. We adopt the construction procedure of the standard English dataset for the task, i.e., FEVER, and improve it for the case of low-resource languages. Specifically, claims are extracted from sentences that are carefully selected to be more informative. The dataset comprises nearly 23K manually-annotated claims. Over 65{\%} of the claims in ParsFEVER are many-hop (require evidence from multiple sources), making the dataset a challenging benchmark (only 13{\%} of the claims in FEVER are many-hop). Also, despite having a smaller training set (around one-ninth of that in Fever), a model trained on ParsFEVER attains similar downstream performance, indicating the quality of the dataset. We release the dataset and the annotation guidelines at \url{https://github.com/Zarharan/ParsFEVER}.
BiQuAD: Towards QA based on deeper text understanding (2021)

Recent question answering and machine reading benchmarks frequently reduce the task to one of pinpointing spans within a certain text passage that answers the given question. Typically, these systems are not required to actually understand the text on a deeper level that allows for more complex reasoning on the information contained. We introduce a new dataset called BiQuAD that requires deeper comprehension in order to answer questions in both extractive and deductive fashion. The dataset consist of 4,190 closed-domain texts and a total of 99,149 question-answer pairs. The texts are synthetically generated soccer match reports that verbalize the main events of each match. All texts are accompanied by a structured Datalog program that represents a (logical) model of its information. We show that state-of-the-art QA models do not perform well on the challenging long form contexts and reasoning requirements posed by the dataset. In particular, transformer based state-of-the-art models achieve F1-scores of only 39.0. We demonstrate how these synthetic datasets align structured knowledge with natural text and aid model introspection when approaching complex text understanding.
Evaluating Universal Dependency Parser Recovery of Predicate Argument Structure via CompChain Analysis (2021)

Accurate recovery of predicate-argument structure from a Universal Dependency (UD) parse is central to downstream tasks such as extraction of semantic roles or event representations. This study introduces compchains, a categorization of the hierarchy of predicate dependency relations present within a UD parse. Accuracy of compchain classification serves as a proxy for measuring accurate recovery of predicate-argument structure from sentences with embedding. We analyzed the distribution of compchains in three UD English treebanks, EWT, GUM and LinES, revealing that these treebanks are sparse with respect to sentences with predicate-argument structure that includes predicate-argument embedding. We evaluated the CoNLL 2018 Shared Task UDPipe (v1.2) baseline (dependency parsing) models as compchain classifiers for the EWT, GUMS and LinES UD treebanks. Our results indicate that these three baseline models exhibit poorer performance on sentences with predicate-argument structure with more than one level of embedding; we used compchains to characterize the errors made by these parsers and present examples of erroneous parses produced by the parser that were identified using compchains. We also analyzed the distribution of compchains in 58 non-English UD treebanks and then used compchains to evaluate the CoNLL`18 Shared Task baseline model for each of these treebanks. Our analysis shows that performance with respect to compchain classification is only weakly correlated with the official evaluation metrics (LAS, MLAS and BLEX). We identify gaps in the distribution of compchains in several of the UD treebanks, thus providing a roadmap for how these treebanks may be supplemented. We conclude by discussing how compchains provide a new perspective on the sparsity of training data for UD parsers, as well as the accuracy of the resulting UD parses.
InFillmore: Frame-Guided Language Generation with Bidirectional Context (2021)

We propose a structured extension to bidirectional-context conditional language generation, or {\textquotedblleft}infilling,{\textquotedblright} inspired by Frame Semantic theory. Guidance is provided through one of two approaches: (1) model fine-tuning, conditioning directly on observed symbolic frames, and (2) a novel extension to disjunctive lexically constrained decoding that leverages frame semantic lexical units. Automatic and human evaluations confirm that frame-guided generation allows for explicit manipulation of intended infill semantics, with minimal loss in distinguishability from human-generated text. Our methods flexibly apply to a variety of use scenarios, and we provide an interactive web demo.
Realistic Evaluation Principles for Cross-document Coreference Resolution (2021)

We point out that common evaluation practices for cross-document coreference resolution have been unrealistically permissive in their assumed settings, yielding inflated results. We propose addressing this issue via two evaluation methodology principles. First, as in other tasks, models should be evaluated on predicted mentions rather than on gold mentions. Doing this raises a subtle issue regarding singleton coreference clusters, which we address by decoupling the evaluation of mention detection from that of coreference linking. Second, we argue that models should not exploit the synthetic topic structure of the standard ECB+ dataset, forcing models to confront the lexical ambiguity challenge, as intended by the dataset creators. We demonstrate empirically the drastic impact of our more realistic evaluation principles on a competitive model, yielding a score which is 33 F1 lower compared to evaluating by prior lenient practices.
Disentangling Online Chats with DAG-structured LSTMs (2021)

Many modern messaging systems allow fast and synchronous textual communication among many users. The resulting sequence of messages hides a more complicated structure in which independent sub-conversations are interwoven with one another. This poses a challenge for any task aiming to understand the content of the chat logs or gather information from them. The ability to disentangle these conversations is then tantamount to the success of many downstream tasks such as summarization and question answering. Structured information accompanying the text such as user turn, user mentions, timestamps, is used as a cue by the participants themselves who need to follow the conversation and has been shown to be important for disentanglement. DAG-LSTMs, a generalization of Tree-LSTMs that can handle directed acyclic dependencies, are a natural way to incorporate such information and its non-sequential nature. In this paper, we apply DAG-LSTMs to the conversation disentanglement task. We perform our experiments on the Ubuntu IRC dataset. We show that the novel model we propose achieves state of the art status on the task of recovering reply-to relations and it is competitive on other disentanglement metrics.
Toward Diverse Precondition Generation (2021)

A typical goal for language understanding is to logically connect the events of a discourse, but often connective events are not described due to their commonsense nature. In order to address this deficit, we focus here on generating precondition events. Precondition generation can be framed as a sequence-to-sequence problem: given a target event, generate a possible precondition. However, in most real-world scenarios, an event can have several preconditions, which is not always suitable for standard seq2seq frameworks. We propose DiP, the Diverse Precondition generation system that can generate unique and diverse preconditions. DiP consists of three stages of the generative process {--} an event sampler, a candidate generator, and a post-processor. The event sampler provides control codes (precondition triggers) which the candidate generator uses to focus its generation. Post-processing further improves the results through re-ranking and filtering. Unlike other conditional generation systems, DiP automatically generates control codes without training on diverse examples. Analysis reveals that DiP improves the diversity of preconditions significantly compared to a beam search baseline. Also, manual evaluation shows that DiP generates more preconditions than a strong nucleus sampling baseline.
One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets (2021)

Semantic parsers map natural language utterances to meaning representations. The lack of a single standard for meaning representations led to the creation of a plethora of semantic parsing datasets. To unify different datasets and train a single model for them, we investigate the use of Multi-Task Learning (MTL) architectures. We experiment with five datasets (Geoquery, NLMaps, TOP, Overnight, AMR). We find that an MTL architecture that shares the entire network across datasets yields competitive or better parsing accuracies than the single-task baselines, while reducing the total number of parameters by 68{\%}. We further provide evidence that MTL has also better compositional generalization than single-task models. We also present a comparison of task sampling methods and propose a competitive alternative to widespread proportional sampling strategies.