Papers by Xin-Yu Dai
Fine-grained Knowledge Fusion for Sequence Labeling Domain Adaptation (D19-1)
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| Challenge: | Existing domain adaptation methods focus on the adaptation from the source domain to the entire target domain without considering the diversity of individual sample samples. |
| Approach: | They propose a fine-grained knowledge fusion model with the domain relevance modeling scheme to control the balance between learning from the target domain data and learning from a source domain model. |
| Outcome: | The proposed model outperforms baselines and state-of-the-art models on three sequence labeling tasks. |
Dynamic Oracle for Neural Machine Translation in Decoding Phase (L18-1)
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| Challenge: | Existing methods to improve NMT performance but there is a discrepancy between training and inference when decoding. |
| Approach: | They propose to use Scheduled Sampling to reduce the discrepancy between training and inference in NMT when decoding to mitigate the discrépancy. |
| Outcome: | The proposed methods improve translation quality over standard NMT system. |
Dynamic Past and Future for Neural Machine Translation (D19-1)
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| Challenge: | Neural machine translation models can benefit from modeling translated and untranslated source contents as recurrent states, but this less interpretable recurrence hinders their power to model dynamic updating of and contents during decoding. |
| Approach: | They propose to model the dynamic updating of and contents during decoding by explicitly separating source words into groups of translated and untranslated contents through parts-to-wholes assignment. |
| Outcome: | The proposed method achieves significant improvements over both Rnmt and Transformer by producing more adequate translations. |
Target-oriented Opinion Words Extraction with Target-fused Neural Sequence Labeling (N19-1)
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| Challenge: | Opinion target extraction and opinion words extraction are two fundamental subtasks in Aspect Based Sentiment Analysis (ABSA). |
| Approach: | They propose a new subtask for Aspect Based Sentiment Analysis to extract opinion words as pairs from a given opinion target. |
| Outcome: | The proposed model outperforms existing methods significantly on several popular ABSA benchmarks. |