Papers by Anqi Zhao
Context-Aware Non-Autoregressive Document-Level Translation with Sentence-Aligned Connectionist Temporal Classification (2024.lrec-main)
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| Challenge: | Existing studies employ autoregressive translation (AT) methods to encode sentences . however, the AT methods struggle with error accumulation when the length of sentences increases. |
| Approach: | They propose a context-aware non-autoregressive framework with the sentence-aligned connectionist temporal classification loss for document-level neural machine translation. |
| Outcome: | The proposed framework achieves 46X speedup on three benchmarks compared to strong baselines. |
MTGP: Multi-turn Target-oriented Dialogue Guided by Generative Global Path with Flexible Turns (2023.findings-acl)
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| Challenge: | Existing approaches focus on global planning, which plans toward the target before the conversation. |
| Approach: | They propose to generate a global path as a natural language sentence instead of a sequence of nodes. |
| Outcome: | The proposed method has fewer turns, more coherent semantics, and higher success rate than baselines. |
A Critical Analysis of Document Out-of-Distribution Detection (2023.findings-emnlp)
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Jiuxiang Gu, Yifei Ming, Yi Zhou, Jason Kuen, Vlad Morariu, Handong Zhao, Ruiyi Zhang, Nikolaos Barmpalios, Anqi Liu, Yixuan Li, Tong Sun, Ani Nenkova
| Challenge: | Existing document understanding models focus on single-modal inputs such as images or texts. |
| Approach: | They propose to use a spatial-aware adapter to adapt transformer-based language models to document domain to exploit multi-modal information. |
| Outcome: | The proposed model significantly improves the OOD detection performance compared to using a standard language model and to competitive baselines. |
Guiding Dialogue Agents to Complex Semantic Targets by Dynamically Completing Knowledge Graph (2023.findings-acl)
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| Challenge: | Existing knowledge graphs are incomplete in tracking complex semantic relations of the target-oriented dialogue. |
| Approach: | They combine methods of knowledge retrieval and relationship prediction to construct a context-related dynamic KG and a metric to evaluate the tracked path automatically. |
| Outcome: | The proposed method can control the agent more logically and smoothly toward the complex target. |