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Found 31 papers, 16 papers with code
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.
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.
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.
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.
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.
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.
Mapping Natural Language Commands to Web Elements
The web provides a rich, open-domain environment with textual, structural, and spatial properties.
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].
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).
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
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.
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 #9 on
Question Answering
on WebQuestions
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.
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.
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.
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.
Ranked #3 on
Semantic Parsing
on CFQ
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.
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.
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.
Improving Compositional Generalization with Latent Structure and Data Augmentation
Generic unstructured neural networks have been shown to struggle on out-of-distribution compositional generalization.
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.
Generate-and-Retrieve: use your predictions to improve retrieval for semantic parsing
Then, it retrieves exemplars with outputs similar to the preliminary prediction which are used to generate a final prediction.
RARR: Researching and Revising What Language Models Say, Using Language Models
Language models (LMs) now excel at many tasks such as few-shot learning, question answering, reasoning, and dialog.
Meta-Learning Fast Weight Language Models
Dynamic evaluation of language models (LMs) adapts model parameters at test time using gradient information from previous tokens and substantially improves LM performance.
Dr.ICL: Demonstration-Retrieved In-context Learning
In-context learning (ICL), teaching a large language model (LLM) to perform a task with few-shot demonstrations rather than adjusting the model parameters, has emerged as a strong paradigm for using LLMs.
PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model Corruptions
These bottlenecks motivate the training of compact editors, which is challenging due to the scarcity of training data for this purpose.
From Pixels to UI Actions: Learning to Follow Instructions via Graphical User Interfaces
Much of the previous work towards digital agents for graphical user interfaces (GUIs) has relied on text-based representations (derived from HTML or other structured data sources), which are not always readily available.
Large Language Models as Analogical Reasoners
Chain-of-thought (CoT) prompting for language models demonstrates impressive performance across reasoning tasks, but typically needs labeled exemplars of the reasoning process.
In-context Learning with Retrieved Demonstrations for Language Models: A Survey
Language models, especially pre-trained large language models, have showcased remarkable abilities as few-shot in-context learners (ICL), adept at adapting to new tasks with just a few demonstrations in the input context.
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.
