Papers by Ionut-Teodor Sorodoc
Probing for Referential Information in Language Models (2020.acl-main)
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| Challenge: | Neural network-based language models (LMs) have been shown to learn relevant properties of language without being explicitly trained for them. |
| Approach: | They extend their previous work to analyze whether language models capture anaphoric relations and pronoun-antecedent relations in English. |
| Outcome: | The Transformer outperforms the LSTM in all analyses. |
Comparatives, Quantifiers, Proportions: a Multi-Task Model for the Learning of Quantities from Vision (N18-1)
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| Challenge: | Comparatives, vague quantification, and proportional estimation are not learned at the same time nor governed by the same rules during language acquisition. |
| Approach: | They propose to combine sets comparison, vague quantification, and proportional estimation to learn them together from visual scenes. |
| Outcome: | The proposed model can generalize to unseen combinations of target/non-target objects. |
What do Entity-Centric Models Learn? Insights from Entity Linking in Multi-Party Dialogue (N19-1)
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| Challenge: | a recent study suggests that models that incorporate a bias towards learning entity representations are not effective at modeling entities. |
| Approach: | They propose to use two entity-centric models for a referential task . they show they outperform the state of the art and do better on lower frequency entities . |
| Outcome: | The proposed models outperform the state of the art on a referential task . they do better on lower frequency entities than a counterpart model not entity-centric . |