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Found 23 papers, 13 papers with code
Retrieval Augmented Language Model Pre-Training
Language model pre-training has been shown to capture a surprising amount of world knowledge, crucial for NLP tasks such as question answering.
Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing
Meanwhile, recent work has shown considerable improvements on many NLP tasks from model scaling.
Improving Compositional Generalization with Latent Structure and Data Augmentation
Generic unstructured neural networks have been shown to struggle on out-of-distribution compositional generalization.
Controllable Semantic Parsing via Retrieval Augmentation
In practical applications of semantic parsing, we often want to rapidly change the behavior of the parser, such as enabling it to handle queries in a new domain, or changing its predictions on certain targeted queries.
Graph-Based Decoding for Task Oriented Semantic Parsing
The dominant paradigm for semantic parsing in recent years is to formulate parsing as a sequence-to-sequence task, generating predictions with auto-regressive sequence decoders.
QA-Driven Zero-shot Slot Filling with Weak Supervision Pretraining
To address this problem, we introduce QA-driven slot filling (QASF), which extracts slot-filler spans from utterances with a span-based QA model.
Unlocking Compositional Generalization in Pre-trained Models Using Intermediate Representations
Sequence-to-sequence (seq2seq) models are prevalent in semantic parsing, but have been found to struggle at out-of-distribution compositional generalization.
Few-shot Intent Classification and Slot Filling with Retrieved Examples
Few-shot learning arises in important practical scenarios, such as when a natural language understanding system needs to learn new semantic labels for an emerging, resource-scarce domain.
Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both?
This has motivated new specialized architectures with stronger compositional biases, but most of these approaches have only been evaluated on synthetically-generated datasets, which are not representative of natural language variation.
Learning Abstract Models for Strategic Exploration and Fast Reward Transfer
Model-based reinforcement learning (RL) is appealing because (i) it enables planning and thus more strategic exploration, and (ii) by decoupling dynamics from rewards, it enables fast transfer to new reward functions.
REALM: Retrieval-Augmented Language Model Pre-Training
Language model pre-training has been shown to capture a surprising amount of world knowledge, crucial for NLP tasks such as question answering.
Ranked #5 on
Question Answering
on WebQuestions
Span-based Hierarchical Semantic Parsing for Task-Oriented Dialog
We propose a semantic parser for parsing compositional utterances into Task Oriented Parse (TOP), a tree representation that has intents and slots as labels of nesting tree nodes.
SPoC: Search-based Pseudocode to Code
Given test cases as a mechanism to validate programs, we search over the space of possible translations of the pseudocode to find a program that passes the validation.
Ranked #2 on
Program Synthesis
on SPoC TestP
Learning Abstract Models for Long-Horizon Exploration
In our approach, a manager maintains an abstract MDP over a subset of the abstract states, which grows monotonically through targeted exploration (possible due to the abstract MDP).
Improving Semantic Parsing for Task Oriented Dialog
Semantic parsing using hierarchical representations has recently been proposed for task oriented dialog with promising results [Gupta et al 2018].
Mapping Natural Language Commands to Web Elements
The web provides a rich, open-domain environment with textual, structural, and spatial properties.
Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration
Reinforcement learning (RL) agents improve through trial-and-error, but when reward is sparse and the agent cannot discover successful action sequences, learning stagnates.
Macro Grammars and Holistic Triggering for Efficient Semantic Parsing
To learn a semantic parser from denotations, a learning algorithm must search over a combinatorially large space of logical forms for ones consistent with the annotated denotations.
From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood
Our goal is to learn a semantic parser that maps natural language utterances into executable programs when only indirect supervision is available: examples are labeled with the correct execution result, but not the program itself.
Inferring Logical Forms From Denotations
A core problem in learning semantic parsers from denotations is picking out consistent logical forms--those that yield the correct denotation--from a combinatorially large space.
Simpler Context-Dependent Logical Forms via Model Projections
With only denotations at training time, we must search over a combinatorially large space of logical forms, which is even larger with context-dependent utterances.
Compositional Semantic Parsing on Semi-Structured Tables
Two important aspects of semantic parsing for question answering are the breadth of the knowledge source and the depth of logical compositionality.
