Papers with high-performance models
Improving Beam Search by Removing Monotonic Constraint for Neural Machine Translation (P18-2)
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| Challenge: | a beam search algorithm produces monotonic left-to-right order, meaning a hypothesis cannot be revisited . a proposed algorithm allows discarded hypotheses to be recovered in a later step. |
| Approach: | They propose to decode a beam search algorithm that considers multiple hypotheses simultaneously . they propose to maintain all found hypothese a single priority queue and a universal score function . |
| Outcome: | The proposed algorithm improves translations even for high-performance models in English-Japanese translation task. |
Flexible Generation from Fragmentary Linguistic Input (2022.acl-long)
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| Challenge: | dominant paradigm for high-performance models in novel language tasks is direct specialization via training from scratch or fine-tuning large pre-trained models. |
| Approach: | They propose a new model that makes it possible to infer human behavior through basic computational motifs. |
| Outcome: | The proposed model outperforms direct-specialization models in three evaluations and performs comparable to human models. |
Measuring Association Between Labels and Free-Text Rationales (2021.emnlp-main)
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| Challenge: | Existing models for extractive rationales do not work as well on reasoning tasks requiring free-text rationale. |
| Approach: | They propose to use pipelines to extract rationales from input words and to use them to explain reasoning tasks. |
| Outcome: | The proposed models exhibit desirable properties for explaining commonsense question-answering and natural language inference, indicating their potential for producing faithful free-text rationales. |
Multilingual Sentence-T5: Scalable Sentence Encoders for Multilingual Applications (2024.lrec-main)
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| Challenge: | Prior work on multilingual sentence embedding has demonstrated that the efficient use of natural language inference data to build high-performance models can outperform conventional methods. |
| Approach: | They propose a multilingual sentence embedding model by extending an existing monolingual model by using the low-rank adaptation technique. |
| Outcome: | The proposed model outperforms the previous approach and shows that languages with fewer resources or those with less linguistic similarity to English benefit more from the parameter increase. |