What is it? Disambiguating the different readings of the pronoun ‘it’

Loáiciga, Sharid; Guillou, Liane; Hardmeier, Christian

doi:10.18653/v1/d17-1137

Cited by 13 publications

(11 citation statements)

References 12 publications

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“…If there are event-favoring properties of the context sentence that human participants are sensitive to, it is a tractable task to build automatic classifiers that learn to recognize such properties. This supports the idea that the task of differentiating anaphoric and pleonastic instances of It (Evans, 2001;Boyd et al, 2005;Bergsma and Yarowsky, 2011;Lee et al, 2016;Loáiciga et al, 2017) could potentially improve performance.…”

Section: Discussionsupporting

confidence: 74%

Event versus entity co-reference: Effects of context and form of referring expression

Loáiciga¹,

Bevacqua²,

Rohde³

et al. 2018

Proceedings of the First Workshop on Computational Models Of Reference, Anaphora and Coreference

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Anaphora resolution systems require both an enumeration of possible candidate antecedents and an identification process of the antecedent. This paper focuses on (i) the impact of the form of referring expression on entity-vsevent preferences and (ii) how properties of the passage interact with referential form. Two crowd-sourced story-continuation experiments were conducted, using constructed and naturally-occurring passages, to see how participants interpret It and This pronouns following a context sentence that makes available event and entity referents. Our participants show a strong, but not categorical, bias to use This to refer to events and It to refer to entities. However, these preferences vary with passage characteristics such as verb class (a proxy in our constructed examples for the number of explicit and implicit entities) and more subtle author intentions regarding subsequent re-mention (the original event-vs-entity re-mention of our corpus items).

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Section: Discussionsupporting

confidence: 74%

Event versus entity co-reference: Effects of context and form of referring expression

Loáiciga¹,

Bevacqua²,

Rohde³

et al. 2018

Proceedings of the First Workshop on Computational Models Of Reference, Anaphora and Coreference

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“…We find that major chunk consisted of 2-6 pronouns in all the 10 questions across the dialog. We tried to distinguish between the usage of 'it' as pleonastic and non-pleonastic pronouns (discussed in (Loáiciga et al, 2017)). For e.g.…”

Section: Diversity and Dialog Phenomena In Visdial Datasetmentioning

confidence: 99%

History for Visual Dialog: Do we really need it?

Agarwal

Bui

Lee

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Visual Dialog involves "understanding" the dialog history (what has been discussed previously) and the current question (what is asked), in addition to grounding information in the image, to generate the correct response. In this paper, we show that co-attention models which explicitly encode dialog history outperform models that don't, achieving state-ofthe-art performance (72 % NDCG on val set). However, we also expose shortcomings of the crowd-sourcing dataset collection procedure by showing that history is indeed only required for a small amount of the data and that the current evaluation metric encourages generic replies. To that end, we propose a challenging subset (VisDialConv) of the VisDial val set and provide a benchmark of 63% NDCG.

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“…Having determined all possible antecedents, the model has to choose the correct one, relying on semantics, syntax, and discourse. The pronoun it can in principle be used as an anaphoric (referring to entities), event reference or pleonastic pronoun (Loáiciga et al, 2017). For the anaphoric it, we identify two major ways of identifying the antecedent: lexical overlap and world knowledge.…”

Section: Coreference Resolutionmentioning

confidence: 99%

ContraCAT: Contrastive Coreference Analytical Templates for Machine Translation

Stojanovski¹,

Krojer²,

Peskov³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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Recent high scores on pronoun translation using context-aware neural machine translation have suggested that current approaches work well. ContraPro is a notable example of a contrastive challenge set for English→German pronoun translation. The high scores achieved by transformer models may suggest that they are able to effectively model the complicated set of inferences required to carry out pronoun translation. This entails the ability to determine which entities could be referred to, identify which entity a sourcelanguage pronoun refers to (if any), and access the target-language grammatical gender for that entity. We first show through a series of targeted adversarial attacks that in fact current approaches are not able to model all of this information well. Inserting small amounts of distracting information is enough to strongly reduce scores, which should not be the case. We then create a new template test set Contracat, designed to individually assess the ability to handle the specific steps necessary for successful pronoun translation. Our analyses show that current approaches to context-aware nmt rely on a set of surface heuristics, which break down when translations require real reasoning. We also propose an approach for augmenting the training data, with some improvements.

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What is it? Disambiguating the different readings of the pronoun ‘it’

Cited by 13 publications

References 12 publications

Event versus entity co-reference: Effects of context and form of referring expression

Event versus entity co-reference: Effects of context and form of referring expression

History for Visual Dialog: Do we really need it?

ContraCAT: Contrastive Coreference Analytical Templates for Machine Translation

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