Papers with bipartite matching
Efficient Hybrid Generation Framework for Aspect-Based Sentiment Analysis (2023.eacl-main)
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| Challenge: | Aspect-based sentiment analysis (ABSA) has attracted broad commercial attention due to its commercial value. |
| Approach: | They propose a framework that generates location and semantic information in parallel and a global hybrid loss function in combination with bipartite matching to achieve end-to-end model training. |
| Outcome: | The proposed framework outperforms state-of-the-art methods in almost all cases and outperfies existing methods in terms of inference efficiency. |
One2Set: Generating Diverse Keyphrases as a Set (2021.acl-long)
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| Challenge: | Recent keyphrase generation models are wrongly imposing a predefined order on keyphrases . a new training paradigm is proposed to concatenate keyphrase sequences in parallel . |
| Approach: | They propose a training paradigm that concatenates keyphrases in a predefined order . they propose combining a fixed set of learned control codes with a bipartite matching mechanism . |
| Outcome: | The proposed model outperforms the state-of-the-art methods on multiple benchmarks. |
OTSeq2Set: An Optimal Transport Enhanced Sequence-to-Set Model for Extreme Multi-label Text Classification (2022.emnlp-main)
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| Challenge: | Extreme multi-label text classification (XMTC) is a task of finding the most relevant subset labels from an extremely large label set. |
| Approach: | They propose an autoregressive sequence-to-set model for XMTC tasks named OTSeq2Set. |
| Outcome: | The proposed model outperforms other competitive baselines on 4 benchmark datasets. |
Set Generation Networks for End-to-End Knowledge Base Population (2021.emnlp-main)
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| Challenge: | Existing knowledge base population systems require a machine translation task to generate multiple facts, but the fact order is not considered. |
| Approach: | They propose a knowledge base population task that aims to discover facts about entities from texts and expand a KB with these facts. |
| Outcome: | The proposed networks achieve state-of-the-art (SoTA) performance on two benchmark datasets. |