Malta

We provide a new linguistic resource: The Minecraft Structured Dialogue Corpus (MSDC), a discourse annotated version of the Minecraft Dialogue Corpus (MDC), with complete discourse structures for the situated dialogues in the style of SDRT. Our structures feature both linguistic discourse moves and nonlinguistic actions.

EACL 2024

Discourse Structure Annotations for the Minecraft Dialogue Corpus

natural language instruction following

corpus creation

discourse structure

Welcome to the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL). Continuing its mission of expanding and involving the science community of all European countries, EACL has selected the Malta community for the 18th EACL. Considering the importance of physical interaction among researchers, the conference will be held at the Hotel Radisson Blu, St. Julians, in Malta, from 17 to 22 of March, 2024. The conference will also feature the possibility to participate virtually. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language. 
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As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

workshop paper

Writing research articles is crucial in any academic's development and is thus an important component of the academic discourse. The Introduction section is often seen as a difficult task within the research article genre. This study presents two metrics of rhetorical moves in academic writing: step-n-grams and lengths of steps. While scholars agree that expert writers follow the general pattern described in the CARS model (Swales, 1990), this study complements previous studies with empirical quantitative data that highlight how writers progress from one rhetorical function to another in practice, based on 50 recent papers by expert writers. The discussion shows the significance of the results in relation to writing instructors and data-driven learning.

Quantitative metrics to the CARS model in academic discourse in biology introductions

With the raise of large language models (LLMs), different evaluation methods, including probing methods, are gaining more attention. Probing methods are meant to evaluate LLMs on their linguistic abilities. However, most of the studies are focused on morphology and syntax, leaving discourse research out of the scope. At the same time, understanding discourse and pragmatics is crucial to building up the conversational abilities of models.

In this paper, we address the problem of probing several models of discourse knowledge in 10 languages. We present an algorithm to automatically adapt existing discourse tasks to other languages based on the Universal Dependencies (UD) annotation. We find that models perform similarly on high- and low-resourced languages. However, the overall low performance of the models' quality shows that they do not acquire discourse well enough.

Probing of pretrained multilingual models on the knowledge of discourse

Discourse relation classification within a multilingual, cross-framework setting is a challenging task, 
and the best-performing systems so far have relied on monolingual and mono-framework approaches.
In this paper, we introduce transformer-based multilingual models, trained jointly over all datasets---thus covering different languages and discourse frameworks. We demonstrate their ability to outperform single-corpus models and to overcome (to some extent) the disparity among corpora, by relying on linguistic features and generic information about the nature of the datasets. We also compare the performance of different multilingual pretrained models, as well as the encoding of the relation direction, a key component for the task. Our results on the 16 datasets of the DISRPT 2021 benchmark show improvements in accuracy in (almost) all datasets compared to the monolingual models, with at best $65.91$\% in average accuracy, thus corresponding to a 4% improvement over the state-of-the-art.

Feature-augmented model for multilingual discourse relation classification

The task of discourse relation recognition within dialogs presents unique challenges. Recent advancements in discourse relation recognition have focused on prompting Pre-Trained Language Models (PLMs) to predict connectives between argument pairs. However, connectives don't play a significant role in dialog, rendering previous approaches employed in monologues less effective. To address this challenge and better leverage knowledge embedded in PLMs, we propose to integrate the Question Under Discussion (QUD) linguistic framework into the task of discourse relation recognition in dialog. We hypothesize that generating free-form questions to represent the relationships between utterances will help with this task. Early findings from this approach are promising, and we plan to continue our investigation.

Exploiting Questions Under Discussion for Discourse Relation Recognition in Dialog

Question Generation (QG), the process of generating meaningful questions from a given context, has proven to be useful for several tasks such as question answering or FAQ generation. While most existing QG techniques generate simple, fact-based questions, this research aims to generate questions that can have complex answers (e.g. "why" questions). We propose a data augmentation method that uses discourse relations to create such questions, and experiment on existing English data. Our approach generates questions based solely on the context without answer supervision, in order to enhance question diversity and complexity. We use an encoder-decoder trained on the augmented dataset to generate either one question or multiple questions at a time, and show that the latter improves over the baseline model when doing a human quality evaluation, without degrading performance according to standard automated metrics.

Complex question generation using discourse-based data augmentation

This paper proposes a classification model for single label implicit discourse relation recognition trained on soft-label distributions. It follows the PDTB 3.0 framework and it was trained and tested on the DiscoGeM corpus, where it achieves an F1-score of 51.38 on third-level sense classification of implicit discourse relations. We argue that training on soft-label distributions allows the model to better discern between more ambiguous discourse relations.

Exploring Soft-Label Training for Implicit Discourse Relation Recognition

The ARRAU corpus is an anaphorically annotated corpus designed to cover a wide variety of aspects of anaphoric reference in a variety of genres, including both written text and spoken language. The objective of this annotation project is to push forward the state of the art in anaphoric annotation, by overcoming the limitations of current annotation practice and the scope of current models of anaphoric interpretation, which in turn may reveal other issues. The resulting corpus is still therefore very much a work in progress almost twenty years after the project started. In this paper, we discuss the issues identified with the coding scheme used for the previous release, ARRAU 2, and through the use of this corpus for three shared tasks; the proposed solutions to these issues; and the resulting corpus, ARRAU 3.

The ARRAU 3.0 Corpus

This study introduces an approach for evaluating the importance of signals proposed by Das and Taboada in discourse parsing. Previous studies using other signals indicate that discourse markers (DMs) are not consistently reliable cues and can act as distractors, complicating relations recognition. The study explores the effectiveness of alternative signal types, such as syntactic and genre-related signals, revealing their efficacy even when not predominant for specific relations. An experiment incorporating RST signals as features for a parser error / success prediction model demonstrates their relevance and provides insights into signal combinations that prevents (or facilitates) accurate relation recognition. The observations also identify challenges and potential confusion posed by specific signals. This study resulted in producing publicly available code and data, contributing to an accessible resources for research on RST signals in discourse parsing.

Signals as Features: Predicting Error/Success in Rhetorical Structure Parsing

Over 15 years ago, Ward and Birner (2006) suggested that non-canonical constructions in English can serve both to mark information status and to structure the information flow of discourse. One such construction is preposing, where a phrasal constituent appears to the left of its canonical position, typically sentence-initially. But computational work on discourse has, to date, ignored non-canonical syntax. We take account of non-canonical syntax by providing quantitative evidence relating NP/PP preposing to discourse relations. The evidence comes from an LLM mask-filling task that compares the predictions when a mask is inserted between the arguments of an implicit inter-sentential discourse relation -- first, when the right-hand argument (Arg2) starts with a preposed constituent, and again, when that constituent is in canonical (post-verbal) position. Results show that (1) the top-ranked mask-fillers in the preposed case agree more often with "gold" annotations in the Penn Discourse Treebank (Webber et al., 2019) than they do in the latter case, and (2) preposing in Arg2 can affect the distribution of discourse-relational senses.

Syntactic Preposing and Discourse Relations

Recent language models have significantly boosted conversational AI by enabling fast and cost-effective response generation in dialogue systems. However, dialogue systems based on neural generative approaches often lack truthfulness, reliability, and the ability to analyze the dialogue flow needed for smooth and consistent conversations with users. To address these issues, we introduce GroundHog, a modified BART architecture, to capture long multi-grained inputs gathered from various factual and linguistic sources, such as Abstract Meaning Representation, discourse relations, sentiment, and grounding information. For experiments, we present an automatically collected dataset from Reddit that includes multi-party conversations devoted to movies and TV series. The evaluation encompasses both automatic evaluation metrics and human evaluation. The obtained results demonstrate that using several linguistic inputs has the potential to enhance dialogue consistency, meaningfulness, and overall generation quality, even for automatically annotated data. We also provide an analysis that highlights the importance of individual linguistic features in interpreting the observed enhancements.

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