| Challenge: | a novel method for capturing graded (and binary) LE is developed for cross-lingual generalisation of lexical entailment . lexicale enlargement is a key principle behind hierarchical structure found in semantic networks . |
| Approach: | They propose a method for cross-lingual generalisation of GR-LE relation using hyperlex and a bilingual dictionary. |
| Outcome: | The proposed method outperforms current state-of-the-art on binary cross-lingual LE detection by a wide margin. |
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Generalized Tuning of Distributional Word Vectors for Monolingual and Cross-Lingual Lexical Entailment (P19-1)
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| Challenge: | Lexical entailment (LE) is a core asymmetric lexical relation that supports tasks like taxonomy induction and text generation. |
| Approach: | They propose a generalized Lexical ENtailment model that captures a specialization function allowing for LE-tuning of the entire distributional vector space and not only the vectors of words seen in lexical constraints. |
| Outcome: | The proposed model improves on graded LE and shows 20% improvement over state-of-the-art LE detection. |
Baselines and Test Data for Cross-Lingual Inference (L18-1)
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| Challenge: | Recent research on textual entailment is limited to English, but it is expanding to other languages. |
| Approach: | They propose to extend the research in SNLI-style natural language inference toward multilingual evaluation by using cross-lingual word embeddings and machine translation. |
| Outcome: | The proposed system scores an average accuracy of just over 75%, but it is not perfect. |
WordNet Is All You Need: A Surprisingly Effective Unsupervised Method for Graded Lexical Entailment (2023.findings-emnlp)
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| Challenge: | a simple unsupervised method for predicting graded lexical entailment in English relies on WordNet . despite its simplicity, our method outperforms all previous methods using WordNet as weak supervision. |
| Approach: | They propose an unsupervised method which relies exclusively on WordNet for predicting graded lexical entailment in English. |
| Outcome: | The proposed method outperforms existing methods on the largest GLE dataset using WordNet. |
Specialising Word Vectors for Lexical Entailment (N18-1)
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| Challenge: | Existing word representation learning methods rely on the distributional hypothesis to learn meaningful word representations. |
| Approach: | They propose a method that emphasises the asymmetric relation of lexical entailment by injecting external linguistic constraints into the input word vector space. |
| Outcome: | The proposed method achieves state-of-the-art in the tasks of hypernymy directionality, hypernomia detection, and graded lexical entailment. |
Lexical Entailment with Hierarchy Representations by Deep Metric Learning (2022.findings-emnlp)
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| Challenge: | Existing lexical entailment studies cannot be applied to words that are not included in the training dataset. |
| Approach: | They propose a method that learns a mapping from word embeddings to hierarchical embedds to predict hypernymy relations among words. |
| Outcome: | The proposed method achieves state-of-the-art performance and robustness for unknown words. |
MultiLexBATS: Multilingual Dataset of Lexical Semantic Relations (2024.lrec-main)
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Dagmar Gromann, Hugo Goncalo Oliveira, Lucia Pitarch, Elena-Simona Apostol, Jordi Bernad, Eliot Bytyçi, Chiara Cantone, Sara Carvalho, Francesca Frontini, Radovan Garabik, Jorge Gracia, Letizia Granata, Fahad Khan, Timotej Knez, Penny Labropoulou, Chaya Liebeskind, Maria Pia Di Buono, Ana Ostroški Anić, Sigita Rackevičienė, Ricardo Rodrigues, Gilles Sérasset, Linas Selmistraitis, Mahammadou Sidibé, Purificação Silvano, Blerina Spahiu, Enriketa Sogutlu, Ranka Stanković, Ciprian-Octavian Truică, Giedre Valunaite Oleskeviciene, Slavko Zitnik, Katerina Zdravkova
| Challenge: | Prior work has focused on analysing lexical semantic relations in word embeddings or probing pretrained language models (PLMs) with some exceptions. |
| Approach: | They propose to use a multilingual parallel dataset of lexical semantic relations adapted from BATS in 15 languages including low-resource languages such as Bambara, Lithuanian, and Albanian as an experiment on cross-lingual transfer of relational knowledge. |
| Outcome: | The proposed dataset is adapted from a BATS-based dataset in 15 languages including low-resource languages such as Bambara, Lithuanian, and Albanian. |
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. |
Cross-lingual Inference with A Chinese Entailment Graph (2022.findings-acl)
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| Challenge: | Existing work on predicate entailment detection from typed open relation triples has not been able to detect predicates. |
| Approach: | They propose a pipeline for building Chinese entailment graphs using an open relation extraction method. |
| Outcome: | The proposed pipeline outperforms monolingual and Chinese entailment graphs on a parallel dataset. |
Probing Cross-Lingual Lexical Knowledge from Multilingual Sentence Encoders (2023.eacl-main)
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| Challenge: | Pretrained multilingual language models (LMs) can be 'rewired' into effective multilingual sentence encoders (SEs) however, it remains unclear how to best leverage them to represent sub-sentence lexical items in cross-lingual lexicals. |
| Approach: | They propose a method for exposing cross-lingual lexical knowledge by additional fine-tuning through inexpensive contrastive learning that requires only a small amount of word translation pairs. |
| Outcome: | The proposed method exposes cross-lingual lexical knowledge by additional fine-tuning through inexpensive contrastive learning that requires only a small amount of word translation pairs. |
The RELX Dataset and Matching the Multilingual Blanks for Cross-Lingual Relation Classification (2020.findings-emnlp)
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| Challenge: | Current approaches for relation classification are focused on the English language and require lots of training data with human annotations. |
| Approach: | They propose a baseline model based on Multilingual BERT and a new multilingual pretraining setup . they propose 'relationship classification' models that use distant supervision . |
| Outcome: | The proposed model significantly improves the baseline model with distant supervision. |