Papers by Melissa Ailem
A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images (D18-1)
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| Challenge: | Existing approaches combine language and perception to infer word embeddings . however, the embeddables produced by such models do not reflect the actual word representations. |
| Approach: | They propose a probabilistic model that integrates linguistic and perceptual inputs to explain observed word-context pairs in a text corpus. |
| Outcome: | The proposed model achieves competitive or stronger results on tasks of assessing pairwise word similarity and image/caption retrieval compared to other state-of-the-art models. |
Examining the robustness of LLM evaluation to the distributional assumptions of benchmarks (2024.acl-long)
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| Challenge: | Using benchmarks to evaluate Large Language Models is inconsistent with the assumption that the test prompts within a benchmark represent a random sample from some real-world distribution of interest. |
| Approach: | They propose to use a model's average performance across the test prompts of a benchmark to evaluate its performance. |
| Outcome: | The results show that the correlation between model performance across test prompts and the test prompt can change model rankings on major benchmarks. |
Encouraging Neural Machine Translation to Satisfy Terminology Constraints (2021.findings-acl)
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| Challenge: | a new approach to encourage neural machine translation to satisfy lexical constraints is proposed . a BLEU score and percentage of generated constraint terms are improved by the proposed method . |
| Approach: | They propose a method that encourages neural machine translation to satisfy lexical constraints at training step . they use a simplified augmentation strategy without source factors and constraint token masking to make it easier to learn the copy behavior . |
| Outcome: | The proposed method improves on baselines in terms of BLEU score and percentage of generated constraint terms. |