| Challenge: | RNNGs are generative models of (tree , string ) pairs that evaluate derivational choices . a non-syntactic neural language model yields no reliable effects . |
| Approach: | They propose to combine a probabilistic generative grammar with a parsing procedure that uses it to manage syntactic derivations as it advances from one word to the next. |
| Outcome: | The proposed model derives two amplitude effects when used against human encephalography data. |
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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. |
Unsupervised Recurrent Neural Network Grammars (N19-1)
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| Challenge: | RNNGs model syntax and structure by incrementally generating a syntax tree and sentence in a top-down, left-to-right order. |
| Approach: | They explore unsupervised learning of recurrent neural network grammars for language modeling and grammar induction. |
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Effective Batching for Recurrent Neural Network Grammars (2021.findings-acl)
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| Challenge: | RNNGs are hard to scale due to the difficulty of batched training. |
| Approach: | They propose to batch RNNGs where every operation is computed in parallel with tensors across multiple sentences. |
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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 . |
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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. |
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Modeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammars (2021.emnlp-main)
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| Challenge: | Existing literature is agnostic about a parsing strategy of hierarchical models . a recent study showed that hierarchically model hierarchic structures capture grammatical dependencies much better than RNNs in targeted syntactic evaluations. |
| Approach: | They evaluated three LMs with head-final left-branching structures and Recurrent Neural Network Grammars with top-down and left-corner parsing strategies as hierarchical models. |
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Multi-Source Syntactic Neural Machine Translation (D18-1)
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| Challenge: | Existing approaches to integrate source syntax into neural machine translations use linearized parses. |
| Approach: | They propose a linearized parsed neural machine translation technique that integrates source syntax into neural machine learning. |
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Explicit Syntactic Guidance for Neural Text Generation (2023.acl-long)
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| Challenge: | Existing text generation models follow the sequence-to-sequence paradigm . generative grammar suggests humans generate language by learning language grammar . |
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How does the brain process syntactic structure while listening? (2023.findings-acl)
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| Challenge: | Syntactic parsing is the task of assigning a syntactical structure to a sentence. |
| Approach: | They investigate the predictive power of the brain encoding models in three settings: individual performance of constituency and dependency parsing based embedding methods, relative effectiveness of each of the syntactic parsers, and relative importance of syntaktic information versus semantic information using BERT embeddngs. |
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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. |