Papers with WMD
EffEval: A Comprehensive Evaluation of Efficiency for MT Evaluation Metrics (2023.findings-emnlp)
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| Challenge: | a recent surge of interest in developing evaluation metrics based on pretrained large language models (LLMs) can better cope with lexical variation. |
| Approach: | They propose to replace computation-intensive transformers with lighter alternatives and employ linear and quadratic approximations for alignment algorithms on top of LLM representations. |
| Outcome: | The proposed approach replaces computation-intensive transformers with lighter alternatives and employs linear and quadratic approximations for alignment algorithms on top of LLM representations. |
Word Mover’s Embedding: From Word2Vec to Document Embedding (D18-1)
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Lingfei Wu, Ian En-Hsu Yen, Kun Xu, Fangli Xu, Avinash Balakrishnan, Pin-Yu Chen, Pradeep Ravikumar, Michael J. Witbrock
| Challenge: | Recent work has demonstrated that Word Mover’s Distance (WMD) that aligns semantically similar words yields unprecedented KNN classification accuracy. |
| Approach: | They propose a Word Mover’s Distance (WMD) method that aligns semantically similar words to generate unsupervised sentences or documents embeddings. |
| Outcome: | The proposed method consistently outperforms state-of-the-art techniques on 9 benchmark text classification datasets and 22 textual similarity tasks. |
Good Meta-tasks Make A Better Cross-lingual Meta-transfer Learning for Low-resource Languages (2023.findings-emnlp)
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| Challenge: | Model-agnostic meta-learning has garnered attention as a promising technique for enhancing few-shot cross-lingual transfer learning in low-resource scenarios. |
| Approach: | They propose a Meta-Task Collector-based Cross-lingual Meta-Transfer framework to adapt data selection strategies to construct cross-lingual meta-tasks to reduce language gaps. |
| Outcome: | The proposed framework significantly improves model performance in the target language with minimal annotation costs. |
The BQ Corpus: A Large-scale Domain-specific Chinese Corpus For Sentence Semantic Equivalence Identification (D18-1)
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| Challenge: | Bank Question corpus is a corpus for sentence semantic equivalence identification (SSEI) because of rich expressions in natural languages, SSEI is really a challenging task. |
| Approach: | They propose to cluster 120,000 question pairs from 1-year online bank custom service logs into stacks by the Word Mover’s Distance (WMD) based Affinity Propagation algorithm to achieve questions with the same intent. |
| Outcome: | The proposed method achieves questions with the same intent by clustering deduplicated questions into stacks by the Word Mover’s Distance (WMD) based Affinity Propagation (AP) algorithm. |
Large-scale similarity search with Optimal Transport (2023.emnlp-main)
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| Challenge: | Word mover's distance (WMD) is a powerful tool for comparing probability distributions in NLP. |
| Approach: | They propose a waterstein distance approximation that uses the L1 embedding method to find the k-nearest neighbors. |
| Outcome: | The proposed approximation performs comparable to the vanilla Wasserstein distance and can be computed three orders of magnitude faster than the vanilla waterstein distance. |
Improving word mover’s distance by leveraging self-attention matrix (2023.findings-emnlp)
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| Challenge: | Word mover's distance (WMD) computes semantic similarity via the optimal alignment between word embeddings, but does not consider word order, making it challenging to distinguish sentences with significant overlaps of similar words. |
| Approach: | They propose to incorporate the sentence structure represented by BERT’s self-attention matrix (SAM) to improve WMD and its variants in paraphrase identification. |
| Outcome: | The proposed method improves WMD and its variants in paraphrase identification with near-equivalent performance in semantic textual similarity. |
A linear time approximation of Wasserstein distance with word embedding selection (2023.emnlp-main)
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| Challenge: | Wasserstein distance is a powerful method for measuring the dissimilarity between distributions and is used in natural language processing to measure dissimilarities between documents. |
| Approach: | They propose a method to combine feature selection and tree approximation of Wasserstein distance to handle high-dimensional problems. |
| Outcome: | The proposed method achieves high performance on document classification using word embeddings and word embeds. |