Papers with formal meaning representations
TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation (D18-2)
Copied to clipboard
| Challenge: | Existing neural semantic parsers only focus on a small subset of tasks, such as SQL queries, robotic commands, and even general-purpose programming languages like Java. |
| Approach: | They propose a transition-based neural semantic parser that maps natural language utterances into formal meaning representations (MRs) they use an abstract syntax description language to constrain the output space and model the information flow. |
| Outcome: | Experiments on four different semantic parsing and code generation tasks show that the proposed system is generalizable, extensible, and effective. |
Text Generation from Discourse Representation Structures (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing models to generate text from formal meaning representations based on Discourse Representation Structures (DRSs) . |
| Approach: | They propose neural models to generate text from formal meaning representations based on Discourse Representation Structures (DRSs). |
| Outcome: | The proposed model achieves competitive performance on the GMB benchmark against several strong baselines. |
Discourse Representation Structure Parsing (P18-1)
Copied to clipboard
| Challenge: | Existing semantic parsers are data-driven using annotated examples consisting of utterances and their meaning representations. |
| Approach: | They propose a method which transforms Discourse Representation Structures (DRSs) to trees and develop a structure-aware model which decomposes the decoding process into three stages. |
| Outcome: | The proposed model outperforms baseline models on the Groningen Meaning Bank (GMB) by a wide margin. |
StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing (P18-1)
Copied to clipboard
| Challenge: | Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures. |
| Approach: | They propose a variational auto-encoding model for semi-supervised semantic parsing which learns from limited amounts of parallel data and readily-available unlabeled NL utterances. |
| Outcome: | Experiments on ATIS domain and Python show that with extra unlabeled data, StructVAE outperforms strong supervised models. |
Jointly Learning Semantic Parser and Natural Language Generator via Dual Information Maximization (P19-1)
Copied to clipboard
| Challenge: | Semantic parsing aims to transform natural language utterances into formal meaning representations (MRs) whereas an NL generator achieves the reverse, the two tasks are often studied separately. |
| Approach: | They propose a method of dual information maximization to regularize the learning process by matching the joint distributions of p and q of NLs. |
| Outcome: | The proposed method empirically maximizes the variational lower bounds of expected joint distributions of NL and MRs. |