Papers by David Weir
Data Augmentation for Hypernymy Detection (2021.eacl-main)
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| Challenge: | Existing methods for supervised inference have limited quality training data. |
| Approach: | They propose two techniques which generate new training examples from existing ones . they combine linguistic principles of hypernym transitivity and intersective modifier-noun composition . |
| Outcome: | The proposed techniques generate new training examples from existing datasets. |
Testing Paraphrase Models on Recognising Sentence Pairs at Different Degrees of Semantic Overlap (2023.starsem-1)
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| Challenge: | Existing models for paraphrase detection are not suitable for many applications . existing datasets ignore and fail to test models in this setup . |
| Approach: | They propose to use adversarial paradigms to test paraphrase detection models . they propose to examine the sensitivity to different degrees of semantic overlap . |
| Outcome: | Empirical results show that paraphrase models and different sentence encoders appear successful on evaluations, but measuring the degree of semantic overlap remains a big challenge for them. |
Leveraging HTML in Free Text Web Named Entity Recognition (2020.coling-main)
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| Challenge: | Named Entity Recognition (NER) is the identification of the proper names of objects. |
| Approach: | They compare HTML tags discarded in free text Named Entity Recognition from Web pages . they find an increased F1 performance for Text+Tags of between 0.9% and 13.2% . |
| Outcome: | The proposed method improves F1 performance over datasets, variants and models. |
Predicate-Argument Based Bi-Encoder for Paraphrase Identification (2022.acl-long)
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| Challenge: | Cross-encoders have been widely applied to sentence pair tasks . bi-encodings have significantly lower computation complexity and are better suited to symmetric tasks. |
| Approach: | They propose a bi-encoder approach to the paraphrase identification task and incorporate predicate-argument information into SBERT. |
| Outcome: | The proposed model outperforms SBERT/SRoBERTa on six paraphrase identification datasets with a minimal increase in parameters. |
MuSeCLIR: A Multiple Senses and Cross-lingual Information Retrieval Dataset (2022.coling-1)
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| Challenge: | Existing datasets for cross-lingual information retrieval (CLIR) are dominated by searches for named entity mentions, which does not provide a good measure for disambiguation performance. |
| Approach: | They propose a dataset to evaluate CLIR systems' disambiguation ability based on polysemous common nouns with multiple possible translations. |
| Outcome: | The proposed dataset shows that it has a higher requirement on the ability of CLIR systems to disambiguate query terms. |
Towards Structure-aware Paraphrase Identification with Phrase Alignment Using Sentence Encoders (2022.coling-1)
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| Challenge: | Existing paraphrase identification datasets exhibit high correlation between positive pairs and the degree of their lexical overlap. |
| Approach: | They propose to combine sentence encoders with an alignment component by representing each sentence as a list of predicate-argument spans and decomposing the sentence-level meaning comparison into the alignment between their spans. |
| Outcome: | The proposed approach improves performance and interpretability for various sentence encoders. |
Representing Syntax and Composition with Geometric Transformations (2021.findings-acl)
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| Challenge: | Existing models of word meaning are based on syntactic rather than proximal co-occurrences, but they are not suitable for syntax sensitive composition. |
| Approach: | They propose to encode syntactic structure by extending the Skip-Gram with Negative sampling architecture from word2vec. |
| Outcome: | The proposed models perform favourably on benchmark word similarity tasks on similarity tests on similar words compared to models based on proximal co-occurrence . however, the real promise of distributional models is the potential for syntax-sensitive composition. |
Testing Large Language Models on Compositionality and Inference with Phrase-Level Adjective-Noun Entailment (2022.coling-1)
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| Challenge: | Existing studies have shown that pre-trained large language models acquire knowledge during pre-training which enables reasoning over relationships between words and more complex inferences over larger units of meaning. |
| Approach: | They propose a benchmark to test compositional entailment models using adjective-noun phrases. |
| Outcome: | The proposed model can generalise well to out–of–distribution sets, since the required knowledge can be stored in the representations of subwords (SW) tokens. |