Papers with BATS
Minimally-Supervised Relation Induction from Pre-trained Language Model (2022.findings-naacl)
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| Challenge: | Existing methods to induce relation in NLP depend heavily on word embeddings. |
| Approach: | They propose a method to induce relation with BERT under minimal supervision . they first extract proper templates from corpus and then use BERT attention weights to represent the pseudo-sentences. |
| Outcome: | The proposed method achieves state-of-the-art in relation induction tasks on Google Analogy Test Sets, Bigger Analogy test set (BATS) and DiffVec. |
Analyzing Word Embedding Through Structural Equation Modeling (2020.lrec-1)
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| Challenge: | Existing studies have shown that word embedding improves accuracy on NLP tasks. |
| Approach: | They propose a causal diagram based on the evaluation results of word embeddings using partial least squares path modeling. |
| Outcome: | The proposed model proves that word embedding contributes to solving downstream tasks. |
IceBATS: An Icelandic Adaptation of the Bigger Analogy Test Set (2022.lrec-1)
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| Challenge: | a new test set that measures word embeddings' ability to recognize linguistic regularities is presented in a paper in elijsson, iran . the test sets are a good quality estimator for extrinsic evaluation of word embedded models . |
| Approach: | They propose a test set that measures language models' ability to recognize linguistic regularities in a balanced way. |
| Outcome: | The proposed set is apt at measuring the capabilities of word embedding models. |
BATS: BenchmArking Text Simplicity 🦇 (2024.findings-acl)
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| Challenge: | Existing studies on text simplification focus on the difference between a source text and its simplified variant. |
| Approach: | They propose to use a dataset to assess the overall simplicity of text. |
| Outcome: | The proposed method compared 15 datasets on text simplification and their impact on the overall simplicity of text. |
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. |