Information Extraction with Differentiable Beam Search on Graph RNNs (2024.lrec-main)
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| Challenge: | Existing approaches to information extraction suffer from exposure bias due to discrepancy between training and decoding. |
| Approach: | They propose to cast graph generation as auto-regressive sequence labeling and make it aware of decoding procedure by using differentiable beam search. |
| Outcome: | The proposed model outperforms its non-decoding-aware version on ACE05 and ConLL04 datasets. |
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Machine Translation Decoding beyond Beam Search (2021.emnlp-main)
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Rémi Leblond, Jean-Baptiste Alayrac, Laurent Sifre, Miruna Pislar, Lespiau Jean-Baptiste, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals
| Challenge: | a new study examines whether beam search can be replaced by a more powerful metric-driven search technique. |
| Approach: | They propose a beam search method which is agnostic to the end metric and report results on a variety of metrics. |
| Outcome: | The proposed method is based on a Monte-Carlo Tree Search (MCTS) based method and shows it can be used in language applications. |
Language-Informed Beam Search Decoding for Multilingual Machine Translation (2024.findings-acl)
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| Challenge: | Beam search decoding is the de-facto method for decoding auto-regressive Neural Machine Translation (NMT) models, but decoding multilingual NMT models produces off-target translations . |
| Approach: | They propose a general decoding algorithm incorporating an off-the-shelf Language Identification (LiD) model into beam search decoding to reduce off-target translations. |
| Outcome: | The proposed language-informed beam search improves +1.1 BLEU and +0.9 BLUE on WMT and OPUS datasets and reduces off-target rates from 22.9% to 7.7% and 65.8% to 25.3% respectively. |
Determinantal Beam Search (2021.acl-long)
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| Challenge: | a new beam search approach allows for a diverse subset selection process . standard beam search does not encode interactions between candidates . |
| Approach: | They propose a beam search reformulation that casts subset selection as the subdeterminant optimization problem. |
| Outcome: | The proposed method offers competitive performance against other diverse set generation strategies while providing a more general approach to optimizing for diversity. |
If beam search is the answer, what was the question? (2020.emnlp-main)
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| Challenge: | surprisingly, beam search results on language generation tasks are low-quality . despite its high error rate, beam searches can be used to decode models with high probability . |
| Approach: | They frame beam search as the exact solution to a different decoding objective . they propose a set of decoding objectives that explicitly enforce this property . |
| Outcome: | The proposed method enforces uniform information density in text, a property motivated by cognitive science. |
Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text Generation (2024.findings-emnlp)
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| Challenge: | Existing approaches to decode text to the most probable sequence have been proposed to address these challenges by improving coherence, diversity, and resemblance to human-generated text. |
| Approach: | They propose a novel decoding strategy that extends contrastive search by incorporating an adaptive degeneration penalty informed by the model’s estimated uncertainty at each generation step. |
| Outcome: | The proposed approach improves creativity and coherence while maintaining coherency across model architectures, languages, and datasets. |
First the Worst: Finding Better Gender Translations During Beam Search (2022.findings-acl)
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| Challenge: | Neural language generation models optimized by likelihood tend towards 'safe' word choice. |
| Approach: | They propose to use beam search to improve gender diversity in n-best lists and rerank n best lists using gender features obtained from the source sentence to address this problem. |
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BeamR: Beam Reweighing with Attribute Discriminators for Controllable Text Generation (2022.findings-aacl)
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| Challenge: | Recent advances in natural language processing have led to the availability of large pre-trained language models with rich generative capabilities. |
| Approach: | They propose a method to combine generative LMs with attribute discriminators to control different attributes of text generation. |
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A Regularization-based Transfer Learning Method for Information Extraction via Instructed Graph Decoder (2024.lrec-main)
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| Challenge: | Existing methods for information extraction (IE) focus on training task-specific models, while common knowledge among different IE tasks is not explicitly modeled. |
| Approach: | They propose a regularization-based transfer learning method for IE via an instructed graph decoder which decodes various complex structures into a graph uniformly based on corresponding instructions. |
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On Decoding Strategies for Neural Text Generators (2022.tacl-1)
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| Challenge: | a recent study suggests that decoding strategies may be more important than the model architecture itself when generating text from probabilistic models. |
| Approach: | They propose to measure changes in attributes of generated text as a function of decoding strategy and task using human and automatic evaluation. |
| Outcome: | The proposed study shows that decoding strategies do not always transfer across tasks . authors show that the differences in attributes are not always consistent across tasks, they say . |
Variable beam search for generative neural parsing and its relevance for the analysis of neuro-imaging signal (D19-1)
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| Challenge: | a variable beam size inference method is proposed for generative parsing for RNNG . the proposed method is not sensitive to lexical biases faced by standard beam search . |
| Approach: | They propose a method of variable beam size inference for Recurrent Neural Network Grammar by drawing inspiration from sequential Monte-Carlo methods such as particle filtering. |
| Outcome: | The proposed method is based on a generative parsing framework that can be used to model brain activity during online sentence comprehension. |