Ireland

We present our study of the syntactic-semantic dependency correlation in Semantic Role Labeling (SRL). We interpret the dependency correlation using the mutual information gain of the hop pattern of Shortest Syntactic Dependency Paths (SSDP). Semantic dependencies in SRL are modeled as a distribution over semantic labels for predicate-argument pairs. We measure the impact of hop patterns on semantic label distributions by the information gain. We find that the information gain for short SSDPs differs significantly, whereas the information gain for long SSDPs approaches zero. The information gain suggests that the semantic label distribution of different hop patterns differs drastically while some hop patterns share similar semantic label distributions. We propose a mixture model-based semantic parser to model the observation. Experiments show that the proposed method successfully learns a cluster assignment reflecting the dependency correlation. The proposed method improves performance in predicting short-distance semantic dependencies while retaining the advantage of syntax-aware methods in predicting long-distance semantic dependencies. The proposed method achieves a small but statistically significant improvement over baseline methods in English, German, and Spanish and obtains competitive performance with state-of-the-art methods in English.

ACL 2022

Modeling Syntactic-Semantic Dependency Correlations in Semantic Role Labeling Using Mixture Models

semantic dependency

syntactic-semantic dependency correlation

syntactic dependency

mixture model

variational inference

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ACL-IJCNLP 2021

findings / work in progress

# Welcome everyone to ACL 2022!

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The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

We investigate what kind of structural knowledge learned in neural network encoders is transferable to processing natural language. We design artificial languages with structural properties that mimic natural language, pretrain encoders on the data, and see how much performance the encoder exhibits on downstream tasks in natural language. Our experimental results show that pretraining with an artificial language with a nesting dependency structure provides some knowledge transferable to natural language. A follow-up probing analysis indicates that its success in the transfer is related to the amount of encoded contextual information and what is transferred is the knowledge of position-aware context dependence of language. Our results provide insights into how neural network encoders process human languages and the source of cross-lingual transferability of recent multilingual language models.

Pretraining with Artificial Language: Studying Transferable Knowledge in Language Models

Thanks to the effectiveness and wide availability of modern pretrained language models (PLMs), recently proposed approaches have achieved remarkable results in dependency-and span-based, multilingual and cross-lingual Semantic Role Labeling (SRL). These results have prompted researchers to investigate the inner workings of modern PLMs with the aim of understanding how, where, and to what extent they encode information about SRL. In this paper, we follow this line of research and probe for predicate argument structures in PLMs. Our study shows that PLMs do encode semantic structures directly into the con-textualized representation of a predicate, and also provides insights into the correlation between predicate senses and their structures, the degree of transferability between nominal and verbal structures, and how such structures are encoded across languages. Finally, we look at the practical implications of such insights and demonstrate the benefits of embedding predicate argument structure information into an SRL model.

Probing for Predicate Argument Structures in Pretrained Language Models

We adopt an evolutionary view on language change in which cognitive factors (in addition to social ones) affect the fitness of words and their success in the linguistic ecosystem. Specifically, we propose a variety of psycholinguistic factors --- semantic, distributional, and phonological --- that we hypothesize are predictive of lexical decline, in which words greatly decrease in frequency over time. 
Using historical data across three languages (English, French, and German), we find that most of our proposed factors show a significant difference in the expected direction between each curated set of declining words and their matched stable words. Moreover, logistic regression analyses show that semantic and distributional factors are significant in predicting declining words. Further diachronic analysis reveals that declining words tend to decrease in the diversity of their lexical contexts over time, gradually narrowing their `ecological niches'.

Quantifying Cognitive Factors in Lexical Decline

When pre-trained contextualized embeddingbased models developed for unstructured data are adapted for structured tabular data, they perform admirably. However, recent probing studies show that these models use spurious correlations, and often predict inference labels by focusing on false evidence or ignoring it altogether. To study this issue, we introduce the task of Trustworthy Tabular Reasoning, where a model needs to extract evidence to be used for reasoning, in addition to predicting the label. As a case study, we propose a twostage sequential prediction approach, which includes an evidence extraction and an inference stage. First, we crowdsource evidence row labels and develop several unsupervised and supervised evidence extraction strategies for InfoTabS, a tabular NLI benchmark. Our evidence extraction strategy outperforms earlier baselines. On the downstream tabular inference task, using only the automatically extracted evidence as the premise, our approach outperforms prior benchmarks.

Right for the Right Reason: Evidence Extraction for Trustworthy Tabular Reasoning

Automatic transfer of text between domains has become popular in recent times. One of its aims is to preserve the semantic content of text being translated from source to target do-main. However, it does not explicitly maintain other attributes between the source and translated text, for e.g., text length and descriptiveness. Maintaining constraints in transfer has several downstream applications, includ- ing data augmentation and debiasing. We introduce a method for such constrained un- supervised text style transfer by introducing two complementary losses to the generative adversarial network (GAN) family of models. Unlike the competing losses used in GANs, we introduce cooperative losses where the discriminator and the generator cooper ate and reduce the same loss. The first is a contrastive loss and the second is a classification loss — aiming to regularize the latent space further and bring similar sentences across domains closer together. We demonstrate that such training retains lexical, syntactic, and domain-specific constraints between domains for multiple benchmark datasets, including ones where more than one attribute change. We show that the complementary cooperative losses improve text quality, according to both automated and human evaluation measures.

So Different Yet So Alike! Constrained Unsupervised Text Style Transfer

Natural language processing (NLP) models trained on people-generated data can be unreliable because, without any constraints, they can learn from spurious correlations that are not relevant to the task. We hypothesize that enriching models with speaker information in a controlled, educated way can guide them to pick up on relevant inductive biases. For the speaker-driven task of predicting code-switching points in English--Spanish bilingual dialogues, we show that adding sociolinguistically-grounded speaker features as prepended prompts significantly improves accuracy. We find that by adding influential phrases to the input, speaker-informed models learn useful and explainable linguistic information. To our knowledge, we are the first to incorporate speaker characteristics in a neural model for code-switching, and more generally, take a step towards developing transparent, personalized models that use speaker information in a controlled way.

Speaker Information Can Guide Models to Better Inductive Biases: A Case Study On Predicting Code-Switching

We contribute a new dataset. The WatClaimCheck dataset is available upon request for non-commercial research purposes only under the fair dealing exception of the Canada Copyright Act.} for the task of automated fact checking and an evaluation of state of the art algorithms. The dataset includes claims (from speeches, interviews, social media and news articles), review articles published by professional fact checkers and premise articles used by those professional fact checkers to support their review and verify the veracity of the claims. An important challenge in the use of premise articles is the identification of relevant passages that will help to infer the veracity of a claim. We show that transferring a dense passage retrieval model trained with review articles improves the retrieval quality of passages in premise articles. We report results for the prediction of claim veracity by inference from premise articles.

WatClaimCheck: A new Dataset for Claim Entailment and Inference

A common task in language processing is to extract the N best hypotheses from a weighted hypothesis space, where the hypotheses are structured objects, for example, strings or trees. We consider two N-best problems on weighted regular tree languages (one for extracting runs and one for extracting trees). In our paper, we present the algorithm Best Trees and prove its correctness and time complexity, and evaluate it both theoretically and empirically against the state-of-the-art method for solving the N-best runs problem, namely Best Runs.

Improved N-Best Extraction with an Evaluation on Language Data

SOCIOFILLMORE is a multilingual tool which helps to bring to the fore the focus or the perspective that a text expresses in depicting an event. Our tool, whose rationale we also support through a large collection of human judgements, is theoretically grounded on frame semantics and cognitive linguistics, and implemented using the LOME frame semantic parser. We describe SOCIOFILLMORE's development and functionalities, show how non-NLP researchers can easily interact with the tool, and present some example case studies which are already incorporated in the system, together with the kind of analysis that can be visualised.

SocioFillmore: A Tool for Discovering Perspectives

Relations between words are governed by hierarchical structure rather than linear ordering. Sequence-to-sequence (seq2seq) models, despite their success in downstream NLP applications, often fail to generalize in a hierarchy-sensitive manner when performing syntactic transformations - for example, transforming declarative sentences into questions. However, syntactic evaluations of seq2seq models have only observed models that were not pre-trained on natural language data before being trained to perform syntactic transformations, in spite of the fact that pre-training has been found to induce hierarchical linguistic generalizations in language models; in other words, the syntactic capabilities of seq2seq models may have been greatly understated. We address this gap using the pre-trained seq2seq models T5 and BART, as well as their multilingual variants mT5 and mBART. We evaluate whether they generalize hierarchically on two transformations in two languages: question formation and passivization in English and German. We find that pre-trained seq2seq models generalize hierarchically when performing syntactic transformations, whereas models trained from scratch on syntactic transformations do not. This result presents evidence for the learnability of hierarchical syntactic information from non-annotated natural language text while also demonstrating that seq2seq models are capable of syntactic generalization, though only after exposure to much more language data than human learners receive.

Modeling Syntactic-Semantic Dependency Correlations in Semantic Role Labeling Using Mixture Models

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Pretraining with Artificial Language: Studying Transferable Knowledge in Language Models

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