Papers by Zhixian Yang
Nearest Neighbor Knowledge Distillation for Neural Machine Translation (2022.naacl-main)
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| Challenge: | k-nearest-neighbor machine translation (kNN-MT) is a state-of-the-art machine translation technique . however, it requires conducting kNN searches for each decoding step, which increases the cost of decoding . |
| Approach: | They propose to move the time-consuming kNN search forward to the preprocessing phase and introduce k Nearest Neighbor Knowledge Distillation (kNN-KD) that trains the base NMT model to directly learn the knowledge of kN. |
| Outcome: | The proposed method improves over the state-of-the-art model while maintaining the same training and decoding speed as the standard model. |
Diversifying Neural Text Generation with Part-of-Speech Guided Softmax and Sampling (2022.coling-1)
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| Challenge: | Existing methods to generate text using contextual features do not consider syntactic structure clues. |
| Approach: | They propose using linguistic annotation, i.e., part-of-speech (POS), to guide the text generation. |
| Outcome: | The proposed method can generate more diverse text while maintaining comparable quality. |
Dependency-based Mixture Language Models (2022.acl-long)
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| Challenge: | Existing models to incorporate syntactic structures into neural language models have relied heavily on elaborate components for a specific language model, which makes them unwieldy in practice to fit into other models. |
| Approach: | They propose a dependency-based mixture language model that incorporates syntactic structures into neural language models by mixing previous dependency modeling probabilities with self-attention. |
| Outcome: | The proposed method can be easily and effectively applied to different neural language models while improving neural text generation on various tasks. |
Exploiting Summarization Data to Help Text Simplification (2023.eacl-main)
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| Challenge: | Existing text simplification datasets are limited to Wikipedia and Newsela, restricting further development of this field. |
| Approach: | They propose an alignment algorithm to extract sentence pairs from summarization datasets and a method to filter suitable pairs. |
| Outcome: | The proposed algorithm can extract sentence pairs from summarization datasets and perform well with real datasets. |