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Found 19 papers, 6 papers with code
Reasoning about Quantities in Natural Language
In order to address these quantitative reasoning problems we first develop a computational approach which we show to successfully recognize and normalize textual expressions of quantities.
Solving General Arithmetic Word Problems
This paper presents a novel approach to automatically solving arithmetic word problems.
Equation Parsing: Mapping Sentences to Grounded Equations
We introduce the problem of Equation Parsing -- given a sentence, identify noun phrases which represent variables, and generate the mathematical equation expressing the relation described in the sentence.
Unit Dependency Graph and its Application to Arithmetic Word Problem Solving
Math word problems provide a natural abstraction to a range of natural language understanding problems that involve reasoning about quantities, such as interpreting election results, news about casualties, and the financial section of a newspaper.
Mapping to Declarative Knowledge for Word Problem Solving
Solving such problems requires the understanding of several mathematical concepts such as dimensional analysis, subset relationships, etc.
Leveraging Past References for Robust Language Grounding
We therefore reframe the grounding problem from the perspective of coreference detection and propose a neural network that detects when two expressions are referring to the same object.
Task-Oriented Dialogue as Dataflow Synthesis
We describe an approach to task-oriented dialogue in which dialogue state is represented as a dataflow graph.
Constrained Language Models Yield Few-Shot Semantic Parsers
We explore the use of large pretrained language models as few-shot semantic parsers.
Value-Agnostic Conversational Semantic Parsing
Conversational semantic parsers map user utterances to executable programs given dialogue histories composed of previous utterances, programs, and system responses.
Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation
We introduce a novel setup for low-resource task-oriented semantic parsing which incorporates several constraints that may arise in real-world scenarios: (1) lack of similar datasets/models from a related domain, (2) inability to sample useful logical forms directly from a grammar, and (3) privacy requirements for unlabeled natural utterances.
BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic Parsing
We introduce BenchCLAMP, a Benchmark to evaluate Constrained LAnguage Model Parsing, that includes context-free grammars for seven semantic parsing datasets and two syntactic parsing datasets with varied output representations, as well as a constrained decoding interface to generate only valid outputs covered by these grammars.
ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models
For each utterance, we prompt the LLM with questions corresponding to its top-level intent and a set of slots and use the LLM generations to construct the target meaning representation.
InstructExcel: A Benchmark for Natural Language Instruction in Excel
With the evolution of Large Language Models (LLMs) we can solve increasingly more complex NLP tasks across various domains, including spreadsheets.
Guided K-best Selection for Semantic Parsing Annotation
Collecting data for conversational semantic parsing is a time-consuming and demanding process.
