Papers by Shilin Wang
DI-BENCH: Benchmarking Large Language Models on Dependency Inference with Testable Repositories at Scale (2025.findings-acl)
Copied to clipboard
Linghao Zhang, Junhao Wang, Shilin He, Chaoyun Zhang, Yu Kang, Bowen Li, Jiaheng Wen, Chengxing Xie, Maoquan Wang, Yufan Huang, Elsie Nallipogu, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
| Challenge: | Existing studies highlight that dependency-related issues cause over 40% of observed runtime errors on the generated repository. |
| Approach: | They propose a large-scale benchmark and evaluation framework specifically designed to assess LLMs’ capability on dependency inference. |
| Outcome: | The proposed model achieves only a 48% execution pass rate on Python, indicating room for improvement. |
Multi-Task Learning with Shared Encoder for Non-Autoregressive Machine Translation (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing non-autoregressive machine translation models have shown significant inference speedup but suffer from inferior translation accuracy. |
| Approach: | They propose to use AT as an auxiliary task to transfer AT knowledge to NAT models by knowledge distillation. |
| Outcome: | The proposed method achieves significant improvements over baseline non-Autoregressive machine translation models on WMT14 En-De and WMT16 En-Ro datasets. |
Mining Word Boundaries from Speech-Text Parallel Data for Cross-domain Chinese Word Segmentation (2025.coling-main)
Copied to clipboard
| Challenge: | Recent studies on Chinese Word Segmentation (CWS) have focused on the cross-domain scenarios, but there is a high cost of manually annotating high-quality data. |
| Approach: | They propose to explicitly mine word boundaries from parallel speech-text data by using the Montreal Forced Aligner toolkit to perform character-level alignment on speech- text data. |
| Outcome: | The proposed approach is based on character-level alignment on speech-text data and a robust complete-then-train (CTT) strategy. |
Data Rejuvenation: Exploiting Inactive Training Examples for Neural Machine Translation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Large-scale training datasets make training neural machine translation models difficult. |
| Approach: | They propose to identify inactive training examples which contribute less to the model performance and introduce data rejuvenation to improve NMT models' training. |
| Outcome: | The proposed framework stabilizes and accelerates the training process of NMT models, resulting in models with better generalization capability. |
Towards Understanding Neural Machine Translation with Word Importance (D19-1)
Copied to clipboard
| Challenge: | Neural machine translation (NMT) has advanced the state-of-the-art on various language pairs, but the interpretability of NMT remains unsatisfactory. |
| Approach: | They propose to attribute NMT output to every input word using a gradient-based method to measure word importance. |
| Outcome: | The proposed method is superior on identifying input words with higher influence on translation performance. |
Few-shot Table-to-text Generation with Prefix-Controlled Generator (2022.coling-1)
Copied to clipboard
| Challenge: | Neural table-to-text generation approaches are data-hungry and lack labeled data. |
| Approach: | They propose a prompt-based approach for few-shot table-to-text generation using a task-specific prefix and an input-specific input prefix. |
| Outcome: | The proposed approach is able to generate table-to-text summaries with a few instances and is validated on human, book and song datasets. |
CopyNE: Better Contextual ASR by Copying Named Entities (2024.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to transcribe contextual named entities (NEs) treat entities as tokens and generate them token-by-token, which may result in incomplete transcriptions of entities. |
| Approach: | They propose a mechanism that can copy entities from the NE dictionary and reduce errors during entity transcription. |
| Outcome: | The proposed mechanism can copy entities from the NE dictionary, reducing errors during entity transcription, ensuring the completeness of the entity. |
XLM-D: Decorate Cross-lingual Pre-training Model as Non-Autoregressive Neural Machine Translation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing pre-training language models have been successful in natural language understanding and autoregressive generation tasks, but non-autoregressive models have not been sufficiently successful. |
| Approach: | They propose a pre-trained masked language model (MLM) and a non-autoregressive generation model with a lightweight decorator. |
| Outcome: | The proposed model outperforms the previous mask-predict model on translation datasets by 19.9x. |