| Challenge: | Semantic parsing is the task of transducing natural language utterances into machine executable meaning representations (e.g., Python code). |
| Approach: | They propose to rerank an n-best list of predicted MRs and use features to fix observed problems with baseline models to improve parser performance. |
| Outcome: | The proposed method outperforms the best published neural parser on four datasets and improves the baseline parsing performance by 5.7% and 2.9%. |
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Neural Reranking for Dependency Parsing: An Evaluation (2020.acl-main)
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| Challenge: | Recent work shows that neural rerankers can improve dependency parsing results over the top k trees produced by a base parser. |
| Approach: | They propose to use a discriminative reranker to improve dependency parsing results . they propose to incorporate global information into the model to improve parse accuracies . |
| Outcome: | The proposed model outperforms existing models on English and German and Czech, and is the only one to improve on German and Chinese data. |
Discriminative Reranking for Neural Machine Translation (2021.acl-long)
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| Challenge: | reranking models allow the integration of rich features to select a better output hypothesis within an n-best list or lattice. |
| Approach: | They use discriminative reranking to train a large transformer architecture to train an ranked list of hypotheses. |
| Outcome: | Experiments on four WMT directions show that discriminative reranking improves translation quality. |
Neural Semantic Parsing (P18-5)
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| Challenge: | Semantic parsing is the study of translating natural language utterances into machine-executable programs. |
| Approach: | They will describe the various approaches researchers have taken to translate natural language into a formal language . they will also discuss why much recent work has chosen to use standard programming languages instead of more linguistically-motivated representations. |
| Outcome: | This paper will describe the various approaches researchers have taken to translate natural language into a formal language. |
Coarse-to-Fine Decoding for Neural Semantic Parsing (P18-1)
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| Challenge: | Experimental results show that semantic parsing is more efficient than using simple decoders. |
| Approach: | They propose a structure-aware neural architecture which decomposes the semantic parsing process into two stages. |
| Outcome: | The proposed architecture consistently improves performance on four datasets characteristic of different domains and meaning representations. |
Compositional Semantic Parsing across Graphbanks (P19-1)
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| Challenge: | Existing semantic parsers that map sentences to graph-based meaning representations are hand-designed for specific graphbanks. |
| Approach: | They propose a compositional neural semantic parser which achieves competitive accuracies across graphbanks. |
| Outcome: | The proposed system achieves competitive accuracies across a variety of graphbanks. |
Benchmarking Meaning Representations in Neural Semantic Parsing (2020.emnlp-main)
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| Challenge: | Existing work on meaning representations is not comprehensively evaluated due to the lack of readily-available execution engines. |
| Approach: | They propose a unified benchmark on meaning representations by integrating existing semantic parsing datasets, completing the missing logical forms, and implementing the missing execution engines. |
| Outcome: | The proposed benchmark combines existing parsing datasets, completes missing logical forms, and implements missing execution engines. |
Balancing Lexical and Semantic Quality in Abstractive Summarization (2023.acl-short)
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| Challenge: | Existing methods to reduce exposure bias in sequence-to-sequence models are underexplored. |
| Approach: | They propose a method to re-rank sequence-to-sequence neural models to reduce exposure bias. |
| Outcome: | The proposed method achieves an 89.67 BERTScore on the CNN/DailyMail and XSum datasets. |
Confidence Modeling for Neural Semantic Parsing (P18-1)
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| Challenge: | Experimental results show that neural semantic parsers are difficult to interpret due to their complexity. |
| Approach: | They propose to use confidence models to estimate predictions for neural semantic parsers . they outline three major causes of uncertainty and use metrics to quantify them . |
| Outcome: | The proposed model outperforms a widely used method that relies on posterior probability and improves interpretation quality. |
Checkpoint Reranking: An Approach to Select Better Hypothesis for Neural Machine Translation Systems (2020.acl-srw)
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| Challenge: | Neural Machine Translation (NMT) has produced excellent results in the field of machine translation due to generation of high-quality translations for different language pairs. |
| Approach: | They propose a method of re-ranking the outputs of Neural Machine Translation systems by focusing on the decoder's ability to generate distinct tokens and without the use of any language model or data. |
| Outcome: | The proposed method achieves translation improvement up to +0.16 BLEU points over baseline. |
Logits Reranking via Semantic Labels for Hard Samples in Text Classification (2024.findings-emnlp)
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| Challenge: | Existing research on text classification models ignores the semantic information inherent in labels, treating them as one-hot vectors. |
| Approach: | They propose a model-agnostic method that leverages label semantics and auto detection of hard samples to improve classification accuracy. |
| Outcome: | The proposed method shows significant improvements across different PLMs. |