Top-Down Structurally-Constrained Neural Response Generation with Lexicalized Probabilistic Context-Free Grammar (N19-1)
Copied to clipboard
| Challenge: | Neural encoder-decoder architectures have shown promise for natural language generation. |
| Approach: | They propose to generate words according to order of first appearance in lexicalized PCFG parse tree . they also combine neural model with symbolic approach to generate syntactic structure . |
| Outcome: | The proposed method improves over sequence-to-sequence baseline in diversity and relevance. |
Similar Papers
The Return of Lexical Dependencies: Neural Lexicalized PCFGs (2020.tacl-1)
Copied to clipboard
| Challenge: | Existing approaches to grammar induction focus on discovering constituents or dependencies. |
| Approach: | They propose to model lexical dependencies using context free grammars instead of lexicals . they show that this unified framework induces both constituents and dependencies . |
| Outcome: | The proposed model overcomes sparsity problems and induces constituents and dependencies better than the current methods. |
Recursive Top-Down Production for Sentence Generation with Latent Trees (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Various studies have shown that incorporating syntactic structures into recursive encoders can be beneficial for various natural language tasks. |
| Approach: | They propose a dynamic programming algorithm that marginalises over latent binary tree structures with N leaves to train a recursive neural function. |
| Outcome: | The proposed model outperforms previous models on the LENGTH split and English question formation tasks on the Multi30k dataset. |
Sentence-Level Content Planning and Style Specification for Neural Text Generation (D19-1)
Copied to clipboard
| Challenge: | Recent advances in text generation systems often produce incoherent and unfaithful outputs . a novel automated text generation system takes into account content selection, text planning, and surface realization. |
| Approach: | They propose an end-to-end trained two-step text generation model that considers sentence-level content planners and language styles. |
| Outcome: | The proposed model outperforms competing models in three domains with diverse topics and varying language styles. |
Explicit Syntactic Guidance for Neural Text Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing text generation models follow the sequence-to-sequence paradigm . generative grammar suggests humans generate language by learning language grammar . |
| Approach: | They propose a syntax-guided generation schema that searches the syntax tree in a top-down direction. |
| Outcome: | The proposed method outperforms autoregressive baselines on paraphrase generation and machine translation. |
Negative Lexically Constrained Decoding for Paraphrase Generation (P19-1)
Copied to clipboard
| Challenge: | Paraphrase generation is a monolingual machine translation problem. |
| Approach: | They propose a neural model that first identifies words in the source sentence that should be paraphrased and then decodes them by negative lexical constraints. |
| Outcome: | The proposed model improves paraphrase generation by making necessary rewrites to an input sentence. |
The Limitations of Limited Context for Constituency Parsing (2021.acl-long)
Copied to clipboard
| Challenge: | a language model that is syntax-aware can produce better samples, authors say . a recent study shows that neural approaches to syntax can perform unsupervised syntactic parsing . |
| Approach: | They propose to incorporate syntax into neural approaches in NLP to produce better samples . they find that the first time neural approaches were able to perform unsupervised syntactic parsing . |
| Outcome: | The proposed models can perform unsupervised syntactic parsing, but they are lagging behind . the proposed models are based on a sandbox of probabilistic context-free-grammars . |
PCFGs Can Do Better: Inducing Probabilistic Context-Free Grammars with Many Symbols (2021.naacl-main)
Copied to clipboard
| Challenge: | Recent work shows that probabilistic context-free grammars with neural parameterization can be effective in unsupervised constituency parsing. |
| Approach: | They propose a parameterization form of PCFGs based on tensor decomposition which has at most quadratic computational complexity in the symbol number. |
| Outcome: | The proposed model improves unsupervised constituency parsing performance across ten languages. |
Neural Bi-Lexicalized PCFG Induction (2021.acl-long)
Copied to clipboard
| Challenge: | Neural lexicalized PCFGs make strong independence assumption on the generation of the child word and thus bilexical dependencies are ignored. |
| Approach: | They propose an approach to parameterize L-PCFGs without making implausible independence assumptions. |
| Outcome: | The proposed approach improves both running speed and unsupervised parsing performance on the English WSJ dataset. |
NEUROSTRUCTURAL DECODING: Neural Text Generation with Structural Constraints (2023.acl-long)
Copied to clipboard
| Challenge: | Current approaches for conditional text generation focus on lexical constraints, but lack syntactic constraints to support complex semantic constraints. |
| Approach: | They propose a decoding algorithm that incorporates syntactic constraints to improve the quality of the generated text. |
| Outcome: | The proposed method improves on three different language generation tasks and shows improved lexical and syntactic metrics. |
Generating Text from Language Models (2023.acl-tutorials)
Copied to clipboard
| Challenge: | a growing percentage of natural language processing tasks focus on the generation of text from probabilistic language models. |
| Approach: | They will provide a centralized discussion of critical considerations when choosing how to generate from a language model. |
| Outcome: | This tutorial will provide a centralized discussion of critical considerations when choosing how to generate from a language model. |