Janosch Haber


2021

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Patterns of Polysemy and Homonymy in Contextualised Language Models
Janosch Haber | Massimo Poesio
Findings of the Association for Computational Linguistics: EMNLP 2021

One of the central aspects of contextualised language models is that they should be able to distinguish the meaning of lexically ambiguous words by their contexts. In this paper we investigate the extent to which the contextualised embeddings of word forms that display multiplicity of sense reflect traditional distinctions of polysemy and homonymy. To this end, we introduce an extended, human-annotated dataset of graded word sense similarity and co-predication acceptability, and evaluate how well the similarity of embeddings predicts similarity in meaning. Both types of human judgements indicate that the similarity of polysemic interpretations falls in a continuum between identity of meaning and homonymy. However, we also observe significant differences within the similarity ratings of polysemes, forming consistent patterns for different types of polysemic sense alternation. Our dataset thus appears to capture a substantial part of the complexity of lexical ambiguity, and can provide a realistic test bed for contextualised embeddings. Among the tested models, BERT Large shows the strongest correlation with the collected word sense similarity ratings, but struggles to consistently replicate the observed similarity patterns. When clustering ambiguous word forms based on their embeddings, the model displays high confidence in discerning homonyms and some types of polysemic alternations, but consistently fails for others.

2020

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Word Sense Distance in Human Similarity Judgements and Contextualised Word Embeddings
Janosch Haber | Massimo Poesio
Proceedings of the Probability and Meaning Conference (PaM 2020)

Homonymy is often used to showcase one of the advantages of context-sensitive word embedding techniques such as ELMo and BERT. In this paper we want to shift the focus to the related but less exhaustively explored phenomenon of polysemy, where a word expresses various distinct but related senses in different contexts. Specifically, we aim to i) investigate a recent model of polyseme sense clustering proposed by Ortega-Andres & Vicente (2019) through analysing empirical evidence of word sense grouping in human similarity judgements, ii) extend the evaluation of context-sensitive word embedding systems by examining whether they encode differences in word sense similarity and iii) compare the word sense similarities of both methods to assess their correlation and gain some intuition as to how well contextualised word embeddings could be used as surrogate word sense similarity judgements in linguistic experiments.

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Assessing Polyseme Sense Similarity through Co-predication Acceptability and Contextualised Embedding Distance
Janosch Haber | Massimo Poesio
Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics

Co-predication is one of the most frequently used linguistic tests to tell apart shifts in polysemic sense from changes in homonymic meaning. It is increasingly coming under criticism as evidence is accumulating that it tends to mis-classify specific cases of polysemic sense alteration as homonymy. In this paper, we collect empirical data to investigate these accusations. We asses how co-predication acceptability relates to explicit ratings of polyseme word sense similarity, and how well either measure can be predicted through the distance between target words’ contextualised word embeddings. We find that sense similarity appears to be a major contributor in determining co-predication acceptability, but that co-predication judgements tend to rate especially less similar sense interpretations equally as unacceptable as homonym pairs, effectively mis-classifying these instances. The tested contextualised word embeddings fail to predict word sense similarity consistently, but the similarities between BERT embeddings show a significant correlation with co-predication ratings. We take this finding as evidence that BERT embeddings might be better representations of context than encodings of word meaning.

2019

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The PhotoBook Dataset: Building Common Ground through Visually-Grounded Dialogue
Janosch Haber | Tim Baumgärtner | Ece Takmaz | Lieke Gelderloos | Elia Bruni | Raquel Fernández
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

This paper introduces the PhotoBook dataset, a large-scale collection of visually-grounded, task-oriented dialogues in English designed to investigate shared dialogue history accumulating during conversation. Taking inspiration from seminal work on dialogue analysis, we propose a data-collection task formulated as a collaborative game prompting two online participants to refer to images utilising both their visual context as well as previously established referring expressions. We provide a detailed description of the task setup and a thorough analysis of the 2,500 dialogues collected. To further illustrate the novel features of the dataset, we propose a baseline model for reference resolution which uses a simple method to take into account shared information accumulated in a reference chain. Our results show that this information is particularly important to resolve later descriptions and underline the need to develop more sophisticated models of common ground in dialogue interaction.