Papers with beam search algorithm
NeuralLog: Natural Language Inference with Joint Neural and Logical Reasoning (2021.starsem-1)
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| Challenge: | Currently, symbolic and deep learning approaches to NLI are receiving less attention. |
| Approach: | They propose a symbolic-based inference framework that integrates symbolic reasoning and semantic formalism to solve NLI tasks. |
| Outcome: | The proposed framework improves accuracy on the NLI task and on the SICK and MED datasets. |
Improving Beam Search by Removing Monotonic Constraint for Neural Machine Translation (P18-2)
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| Challenge: | a beam search algorithm produces monotonic left-to-right order, meaning a hypothesis cannot be revisited . a proposed algorithm allows discarded hypotheses to be recovered in a later step. |
| Approach: | They propose to decode a beam search algorithm that considers multiple hypotheses simultaneously . they propose to maintain all found hypothese a single priority queue and a universal score function . |
| Outcome: | The proposed algorithm improves translations even for high-performance models in English-Japanese translation task. |
Collective Entity Disambiguation with Structured Gradient Tree Boosting (N18-1)
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| Challenge: | Existing work on structured gradient tree boosting for collective entity disambiguation is limited to regular classification or regression problems. |
| Approach: | They propose a structured learning model that uses gradient tree boosting to disambiguate named entities in a document. |
| Outcome: | The proposed model outperforms the previous state-of-the-art neural system by near 1% absolute accuracy on the popular AIDA-CoNLL dataset. |
Decipherment of Substitution Ciphers with Neural Language Models (D18-1)
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| Challenge: | Existing methods for deciphering homophonic substitution ciphers use pre-trained neural LMs. |
| Approach: | They propose a beam search algorithm that scores the entire candidate plaintext at each step of the decipherment using a neural language model. |
| Outcome: | The proposed beam search algorithm improves on challenging ciphers with smaller beam sizes and better error rates than state-of-the-art methods. |
Speculative Beam Search for Simultaneous Translation (D19-1)
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| Challenge: | Beam search is widely used in (full-sentence) machine translation but its application to simultaneous translation remains highly non-trivial. |
| Approach: | They propose a beam search algorithm that hallucinates several steps into the future to reach a more accurate decision by implicitly benefiting from a target language model. |
| Outcome: | The proposed method improves on language models over diverse language pairs and shows significant improvements over greedy search. |
Neural Generative Rhetorical Structure Parsing (D19-1)
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| Challenge: | Existing approaches to RST parsing use discriminative models, but these are less sample efficient and datasets are typically small. |
| Approach: | They propose a document-level RNN grammar with a bottom-up traversal order and a beam search algorithm that keeps track of both structure-and word-generating actions without exhibit-ing this branching bias. |
| Outcome: | The proposed model outperforms a discriminative model with the same features by 2.6 F1points and achieves comparable performance to the state-of-the-art. |
Benchmarking and Improving Text-to-SQL Generation under Ambiguity (2023.emnlp-main)
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| Challenge: | Existing decoding algorithms treat SQL queries as a string and produce unhelpful token-level diversity in the top-k. |
| Approach: | They propose a benchmarking algorithm that generates all SQLs in top-k ranked outputs . they use plan-based template generation and constrained infilling to bridge this gap . |
| Outcome: | The proposed algorithm is 2.5 times more effective than state-of-the-art models at generating all candidate SQLs in the top-k ranked outputs. |