| 1. |
- Beloucif, Meriem, et al.
(författare)
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Elvis vs. M. Jackson : Who has More Albums? Classification and Identification of Elements in Comparative Questions
- 2022
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Ingår i: LREC 2022. - : European Language Resources Association. - 9791095546726 ; , s. 3771-3779
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Konferensbidrag (refereegranskat)abstract
- Comparative Question Answering (cQA) is the task of providing concrete and accurate responses to queries such as: "Is Lyft cheaper than a regular taxi?" or "What makes a mortgage different from a regular loan?". In this paper, we propose two new open-domain real-world datasets for identifying and labeling comparative questions. While the first dataset contains instances of English questions labeled as comparative vs. non-comparative, the second dataset provides additional labels including the objects and the aspects of comparison. We conduct several experiments that evaluate the soundness of our datasets. The evaluation of our datasets using various classifiers show promising results that reach close-to-human results on a binary classification task with a neural model using ALBERT embeddings. When approaching the unsupervised sequence labeling task, some headroom remains.
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| 2. |
- Beloucif, Meriem, et al.
(författare)
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Probing Pre-trained Language Models for Semantic Attributes and their Values
- 2021
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Ingår i: Findings of the Association for Computational Linguistics: EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 16-20 November, 2021. - Stroudsburg, PA, USA : Association for Computational Linguistics. - 9781955917100 ; , s. 2554-2559
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Konferensbidrag (refereegranskat)abstract
- Pretrained Language Models (PTLMs) yield state-of-the-art performance on many Natural Language Processing tasks, including syntax, semantics and commonsense reasoning. In this paper, we focus on identifying to what extent do PTLMs capture semantic attributes and their values, e.g. the relation between rich and high net worth. We use PTLMs to predict masked tokens using patterns and lists of items from Wikidata in order to verify how likely PTLMs encode semantic attributes along with their values. Such inferences based on semantics are intuitive for us humans as part of our language understanding. Since PTLMs are trained on large amounts of Wikipedia data, we would assume that they can generate similar predictions. However, our findings reveal that PTLMs perform still much worse than humans on this task. We show an analysis which explains how to exploit our methodology to integrate better context and semantics into PTLMs using knowledge bases.
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| 3. |
- Beloucif, Meriem, et al.
(författare)
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Using Wikidata for Enhancing Compositionality in Pre-trained Language Models
- 2023
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Ingår i: Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing. - : INCOMA. - 9789544520922 ; , s. 170-178
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Konferensbidrag (refereegranskat)abstract
- One of the many advantages of pre-trained language models (PLMs) such as BERT and RoBERTa is their flexibility and contextual nature. These features give PLMs strong capabilities for representing lexical semantics. However, PLMs seem incapable of capturing high-level semantics in terms of compositionally. We show that when augmented with the relevant semantic knowledge, PMLs learn to capture a higher degree of lexical compositionality. We annotate a large dataset from Wikidata highlighting a type of semantic inference that is easy for humans to understand but difficult for PLMs, like the correlation between age and date of birth. We use this resource for finetuning DistilBERT, BERT large and RoBERTa. Our results show that the performance of PLMs against the test data continuously improves when augmented with such a rich resource. Our results are corroborated by a consistent improvement over most GLUE benchmark natural language understanding tasks.
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| 4. |
- Bondarenko, Alexander, et al.
(författare)
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Overview of Touché 2022 : Argument Retrieval
- 2022
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Ingår i: Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2022). - Cham : Springer Nature. - 9783031136436 - 9783031136429 ; , s. 311-336
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Konferensbidrag (refereegranskat)abstract
- This paper is a condensed report on the third year of the Touche lab on argument retrieval held at CLEF 2022. With the goal to foster and support the development of technologies for argument mining and argument analysis, we organized three shared tasks in the third edition of Touche: (a) argument retrieval for controversial topics, where participants retrieve a gist of arguments from a collection of online debates, (b) argument retrieval for comparative questions, where participants retrieve argumentative passages from a generic web crawl, and (c) image retrieval for arguments, where participants retrieve images from a focused web crawl that show support or opposition to some stance.
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