Papers by Pengyu Ji
Unsupervised Morphological Tree Tokenizer (2025.findings-acl)
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| Challenge: | Conventional statistical tokenizers often disrupt constituent boundaries within words, thereby corrupting semantic information. |
| Approach: | They propose a method that uses morphological structure guidance to induce character-level structures of words by training a deep model. |
| Outcome: | Empirical results show that the proposed method retains complete morphemes and outperforms existing methods on morphological segmentation and language modeling tasks. |
Tree-Structured Non-Autoregressive Decoding for Sequence-to-Sequence Text Generation (2025.findings-emnlp)
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| Challenge: | Autoregressive Transformers suffer from high inference latency due to sequential token generation. |
| Approach: | They propose a tree-structured non-autoregressive decoding paradigm that bridges autoregressive and non-automatic decoding. |
| Outcome: | The proposed paradigm outperforms autoregressive and non-autoregressive decoding in machine translation and paraphrase generation. |
Generative Pretrained Structured Transformers: Unsupervised Syntactic Language Models at Scale (2024.acl-long)
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| Challenge: | Existing syntactic language models require a gold tree and sequential training to generate sentences. |
| Approach: | They propose an unsupervised syntactic language model that incrementally generates a sentence with its syntaktic tree in a left-to-right manner. |
| Outcome: | The proposed model outperforms existing models on grammar induction and comprehension tasks while holding a substantial acceleration on training. |
Improving Span Representation by Efficient Span-Level Attention (2023.findings-emnlp)
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| Challenge: | Existing methods for generating high-quality span representations are limited by subset of tokens . span-span interactions should play an important role in span encoding, authors argue . |
| Approach: | They propose to introduce span-span interactions and more comprehensive span-token interactions to improve span representations. |
| Outcome: | The proposed model outperforms baseline models on span-related tasks and shows superior performance. |