Papers with PCFG
A Fast Algorithm for Computing Prefix Probabilities (2023.acl-short)
Copied to clipboard
| Challenge: | Probabilistic context-free grammars are an important formalism in NLP . |
| Approach: | They propose to run a probabilistic context-free grammar in O(n3|N|3 + |N|4), where n is the input length and |N is the number of non-terminals in the grammar. |
| Outcome: | The proposed algorithm runs in O(n3|N|3 + |N|4), where n is the input length and |N | is the number of non-terminals in the grammar. |
PCFG-Based Natural Language Interface Improves Generalization for Controlled Text Generation (2023.starsem-1)
Copied to clipboard
| Challenge: | Existing work on controlled text generation (CTG) assumes a control interface of categorical attributes. |
| Approach: | They propose a natural language interface to embed control attributes into natural language commands and propose variants of existing CTG models that take commands as input. |
| Outcome: | The proposed model can generalize to unseen attributes and unsealed attribute combinations. |
The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies show that basic configurations can improve the performance of neural networks on systematic generalization. |
| Approach: | They propose to revisit basic configurations to improve the performance of Transformers on systematic generalization by revisiting scaling of embeddings, early stopping, relative positional embeddment, and Universal Transformer variants. |
| Outcome: | The proposed models improve accuracy from 50% to 85% on the PCFG productivity split and from 35% to 81% on COGS. |
Generalized chart constraints for efficient PCFG and TAG parsing (P18-2)
Copied to clipboard
| Challenge: | Existing pruning techniques limit chart constraints to PCFGs and cannot be applied to more expressive grammars. |
| Approach: | They propose to apply chart constraints to more expressive grammars and a neural tagger which predicts chart constraints at very high precision. |
| Outcome: | The proposed technique accelerates both PCFG and TAG parsing by two orders of magnitude while improving accuracy. |
Program Synthesis for Complex QA on Charts via Probabilistic Grammar Based Filtered Iterative Back-Translation (2023.findings-eacl)
Copied to clipboard
Shabbirhussain Bhaisaheb, Shubham Paliwal, Rajaswa Patil, Manasi Patwardhan, Lovekesh Vig, Gautam Shroff
| Challenge: | Current chart-based Question Answering approaches address structural, visual or simple data retrieval-type questions with fixed-vocabulary answers. |
| Approach: | They employ a neural semantic parser to transform NL questions into SQL programs . they use a probabilistic context-free grammar to generate NL queries from a schema . |
| Outcome: | The proposed approach achieves State-of-the-Art (SOTA) results on reasoning-based queries. |
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 . |
Learning to Synthesize Data for Semantic Parsing (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing methods for synthesizing data for semantic parsing require handcrafted rules to synthesize new programs or utterance-program pairs. |
| Approach: | They propose to use a (non-neural) PCFG to model the composition of programs and a BART-based translation model to map a program to an utterance to learn a generative model from existing data. |
| Outcome: | The proposed model can be efficiently learned from existing data on benchmarks of GeoQuery and Spider. |
Making Transformers Solve Compositional Tasks (2022.acl-long)
Copied to clipboard
| Challenge: | Several studies have reported the inability of Transformer models to generalize compositionally . a key aspect of natural language is the ability to learn basic primitives . |
| Approach: | They propose to use Transformers to generalize compositionally in a large range of tasks . they find that Transformers generalize significantly better than previous models . |
| Outcome: | The proposed models generalize compositionally significantly better than previous models . a set of 12 datasets shows that the proposed models can be improved . |
Informativeness and Invariance: Two Perspectives on Spurious Correlations in Natural Language (2022.naacl-main)
Copied to clipboard
| Challenge: | Spurious correlations are a threat to the trustworthiness of natural language processing systems. |
| Approach: | They propose a definition of spurious correlations in terms of conditional probabilities and a generalized definition of the term . they propose UIs that allow individual input features to be independent of labels. |
| Outcome: | The proposed definition can be generalized from uniformity to independence without affecting the claims of the paper. |
Using Artificial French Data to Understand the Emergence of Gender Bias in Transformer Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies have demonstrated the ability of neural language models to learn linguistic properties without direct supervision. |
| Approach: | They propose to use an artificial corpus generated by a PCFG to control the gender distribution in training data and determine under which conditions a model correctly captures gender information. |
| Outcome: | The proposed approach allows to control the gender distribution in training data and determine under which conditions a model correctly captures gender information or appears gender-biased. |