| Challenge: | Existing approaches to measure textual similarity are inconsistent with the word alignment and are empirically inferior to the simple cosine similarity between general-purpose sentence vectors. |
| Approach: | They propose to decouple word vectors into their norm and direction and then grow the norm and directions of word vector. |
| Outcome: | The proposed methods outperform alignment-based approaches on several benchmarks and strong baselines on the semantic textual similarity task. |
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| Challenge: | Using word similarities, we improve word alignments in low resource settings . word alignment is essential for statistical machine translation (MT) |
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Correlation Coefficients and Semantic Textual Similarity (N19-1)
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What just happened? Evaluating retrofitted distributional word vectors (N19-1)
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| Challenge: | Word embeddings are geometrical representations of word paradigmatics and syntagmatics. |
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Unbalanced Optimal Transport for Unbalanced Word Alignment (2023.acl-long)
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| Challenge: | Figure 1 illustrates the challenges of monolingual word alignment. |
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