Neural Compositional Denotational Semantics for Question Answering

By Nitish Gupta, Michael Lewis. 2018

23 citations recorded by the source corpus.

Answering compositional questions requiring multi-step reasoning is challenging. We introduce an end-to-end differentiable model for interpreting questions about a knowledge graph (KG), which is inspired by formal approaches to semantics. Each span of text is represented by a denotation in a KG and a vector that captures ungrounded aspects of meaning. Learned composition modules recursively combine constituent spans, culminating in a grounding for the complete sentence which answers the question. For example, to interpret “not green”, the model represents “green” as a set of KG entities and “not” as a trainable ungrounded vector—and then uses this vector to parameterize a composition function that performs a complement operation. For each sentence, we build a parse chart subsuming all possible parses, allowing the model to jointly learn both the composition operators and output structure by gradient descent from end-task supervision. The model learns a variety of challenging semantic operators, such as quantifiers, disjunctions and composed relations, and infers latent syntactic structure. It also generalizes well to longer questions than seen in its training data, in contrast to RNN, its tree-based variants, and semantic parsing baselines.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

Break It Down: A Question Understanding Benchmark (2020, background): “Question Decomposition Recent work on QA through question decomposition has focused mostly on single modalities (Gupta and Lewis, 2018; Guo et al., 2019; Min et al., 2019b).”
Neural Module Networks for Reasoning over Text (2019, background): “Gupta & Lewis (2018) propose a NMN model for QA against knowledge graphs and learn execution for semantic operators from QA supervision alone.”
Is a Question Decomposition Unit All We Need? (2022, unknown): “Also, knowledge graphs are combined with neural networks to generate decompositions (Gupta and Lewis, 2018).”
Span-based Semantic Parsing for Compositional Generalization (2020, background): “, 2019), and proposing specialized architectures mainly for the SCAN task (Lake, 2019; Nye et al., 2020; Gordon et al., 2020; Liu et al., 2020; Gupta and Lewis, 2018).”