Articles

AbstractIn this paper, we describe the submission of Dublin City University (DCU) and Trinity College Dublin (TCD) for the WebNLG 2023 shared task. We present a fully rule-based pipeline for generating Irish texts from DBpedia triple sets which comprises 4 components: triple lexicalisation, generation of noninflected Irish text, inflection generation, …...

AbstractLLMs are great at tasks involving English which dominates in their training data. We explore their ability to address tasks involving languages that are severely under-represented in their training data. More specifically, we do this in the context of data-to-text generation for Irish, Maltese, Welsh and Breton. During the prompt-engineering …...

AbstractIn this paper, we conduct the first study on spurious correlations for open-domain response generation models based on a corpus CGDialog curated by ourselves. The current models indeed suffer from spurious correlations and have a tendency to generate irrelevant and generic responses. Inspired by causal discovery algorithms, we propose a …...

AbstractRecent work has shown that Aspect-Term Sentiment Analysis (ATSA) can be effectively performed by Gradual Machine Learning (GML). However, the performance of the current unsupervised solution is limited by inaccurate and insufficient knowledge conveyance. In this paper, we propose a supervised GML approach for ATSA, which can effectively exploit labeled …...

AbstractWhile many real-life tasks require reasoning over multi-step sequential instructions, collecting fine-grained annotations for each intermediate step can be prohibitively expensive. In this work, we study how general pretrained sequence-to-sequence transformers perform under varying types of annotation for sequential instruction understanding. We conduct experiments using T5 (Raffel et al., 2020) …...

AbstractThis paper begins with the premise that adverbs are neglected in computational linguistics. This view derives from two analyses: a literature review and a novel adverb dataset to probe a state-of-the-art language model, thereby uncovering systematic gaps in accounts for adverb meaning. We suggest that using Frame Semantics for characterizing …...

AbstractStance detection deals with identifying an author’s stance towards a target. Most existing stance detection models are limited because they do not consider relevant contextual information which allows for inferring the stance correctly. Complementary context can be found in knowledge bases but integrating the context into pretrained language models is …...

AbstractWords of Estimative Probability (WEP) are phrases used to express the plausibility of a statement. Examples include terms like \textit{probably, maybe, likely, doubt, unlikely}, and \textit{impossible}. Surveys have shown that human evaluators tend to agree when assigning numerical probability levels to these WEPs. For instance, the term \textit{highly likely} equates …...

AbstractTransformers have been shown to work well for the task of English euphemism disambiguation, in which a potentially euphemistic term (PET) is classified as euphemistic or non-euphemistic in a particular context. In this study, we expand on the task in two ways. First, we annotate PETs for vagueness, a linguistic …...

AbstractWe tackle the problem of monolingual phrase alignment conforming to syntactic structures. The existing method formalises the problem as unordered tree mapping; hence, the alignment quality is easily affected by syntactic ambiguities. We address this problem by expanding the method to align parse forests rather than 1-best trees, where syntactic …...

AbstractIn this study, we propose using the GPT-3 as a query generator for the backend of CLIP as an implicit word sense disambiguation (WSD) component for the SemEval 2023 shared task Visual Word Sense Disambiguation (VWSD). We confirmed previous findings — human-like prompts adapted for WSD with quotes benefit both …...

AbstractPrior work typically describes out-of-domain (OOD) or out-of-distribution (OODist) samples as those that originate from dataset(s) or source(s) different from the training set but for the same task. When compared to in-domain (ID) samples, the models have been known to usually perform poorer on OOD samples, although this observation is …...

AbstractState-of-the-art pretrained language models tend to perform below their capabilities when applied out-of-the-box on tasks that require understanding and working with numbers (usually referred to as numeracy). Recent work suggests two main reasons for this: (1) popular tokenisation algorithms have limited expressiveness for numbers, and (2) common pretraining objectives do …...

AbstractIn this paper we investigate the application of active learning to semantic role labeling (SRL) using Bayesian Active Learning by Disagreement (BALD). Our new predicate-focused selection method quickly improves efficiency on three different specialised domain corpora. This is encouraging news for researchers wanting to port SRL to domain specific applications. …...

AbstractIn this work we build upon negative results from an attempt at language modeling with predicted semantic structure, in order to establish empirical lower bounds on what could have made the attempt successful. More specifically, we design a concise binary vector representation of semantic structure at the lexical level and …...

AbstractThe task of entity state tracking aims to automatically analyze procedural texts – texts that describe a step-by-step process (e.g. a baking recipe). Specifically, the goal is to track various states of the entities participating in a given process. Some of the challenges for this NLP task include annotated data …...

AbstractThe representation space of pretrained Language Models (LMs) encodes rich information about words and their relationships (e.g., similarity, hypernymy, polysemy) as well as abstract semantic notions (e.g., intensity). In this paper, we demonstrate that lexical stylistic notions such as complexity, formality, and figurativeness, can also be identified in this space. …...

AbstractPrior work has shown that coupling sequential latent variable models with semantic ontological knowledge can improve the representational capabilities of event modeling approaches. In this work, we present a novel, doubly hierarchical, semi-supervised event modeling framework that provides structural hierarchy while also accounting for ontological hierarchy. Our approach consistsof multiple …...

AbstractFunctional Distributional Semantics is a linguistically motivated framework for modelling lexical and sentence-level semantics with truth-conditional functions using distributional information. Previous implementations of the framework focus on subjectverbobject (SVO) triples only, which largely limits the contextual information available for training and thus the capability of the learnt model. In this …...

AbstractLexical Semantic Change is the study of how the meaning of words evolves through time. Another related question is whether and how lexical relations over pairs of words, such as synonymy, change over time. There are currently two competing, apparently opposite hypotheses in the historical linguistic literature regarding how synonymous …...