Search Results for author:
Found 55 papers, 28 papers with code
Bridging the Generalization Gap in Text-to-SQL Parsing with Schema Expansion
Text-to-SQL parsers map natural language questions to programs that are executable over tables to generate answers, and are typically evaluated on large-scale datasets like Spider (Yu et al., 2018).
Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms
By leveraging data from multiple clients, the FL paradigm can be especially beneficial for clients that have little training data to develop a data-hungry neural semantic parser on their own.
Error Detection for Text-to-SQL Semantic Parsing
Despite remarkable progress in text-to-SQL semantic parsing in recent years, the performance of existing parsers is still far from perfect.
Adaptive Chameleon or Stubborn Sloth: Unraveling the Behavior of Large Language Models in Knowledge Clashes
By providing external information to large language models (LLMs), tool augmentation (including retrieval augmentation) has emerged as a promising solution for addressing the limitations of LLMs' static parametric memory.
Text-to-SQL Error Correction with Language Models of Code
Thus, we propose a novel representation for SQL queries and their edits that adheres more closely to the pre-training corpora of language models of code.
Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors
Recent work has shown that fine-tuning large language models (LLMs) on large-scale instruction-following datasets substantially improves their performance on a wide range of NLP tasks, especially in the zero-shot setting.
Ranked #2 on
Relation Extraction
on TACRED-Revisited
Memorization for Good: Encryption with Autoregressive Language Models
Over-parameterized neural language models (LMs) can memorize and recite long sequences of training data.
Automatic Evaluation of Attribution by Large Language Models
To facilitate the evaluation, we manually curate a set of test examples covering 12 domains from a generative search engine, New Bing.
PiML Toolbox for Interpretable Machine Learning Model Development and Validation
PiML (read $\pi$-ML, /`pai.`em.`el/) is an integrated and open-access Python toolbox for interpretable machine learning model development and model diagnostics.
Few-shot In-context Learning for Knowledge Base Question Answering
On GrailQA and WebQSP, our model is also on par with other fully-trained models.
Quiz-based Knowledge Tracing
In this paper, we present the Quiz-based Knowledge Tracing (QKT) model to monitor students' knowledge states according to their quiz-based learning interactions.
Privacy-Preserving Domain Adaptation of Semantic Parsers
Task-oriented dialogue systems often assist users with personal or confidential matters.
A Retrieve-and-Read Framework for Knowledge Graph Link Prediction
To address the limitations of existing KG link prediction frameworks, we propose a novel retrieve-and-read framework, which first retrieves a relevant subgraph context for the query and then jointly reasons over the context and the query with a high-capacity reader.
Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments
Most existing work for grounded language understanding uses LMs to directly generate plans that can be executed in the environment to achieve the desired effects.
LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models
In this work, we propose a novel method, LLM-Planner, that harnesses the power of large language models to do few-shot planning for embodied agents.
Hand Hygiene Assessment via Joint Step Segmentation and Key Action Scorer
To address this issue, we design a multi-stage convolution-transformer network for step segmentation.
Knowledge Base Question Answering: A Semantic Parsing Perspective
In this survey, we situate KBQA in the broader literature of semantic parsing and give a comprehensive account of how existing KBQA approaches attempt to address the unique challenges.
Bootstrapping a User-Centered Task-Oriented Dialogue System
We present TacoBot, a task-oriented dialogue system built for the inaugural Alexa Prize TaskBot Challenge, which assists users in completing multi-step cooking and home improvement tasks.
When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems
Rejecting class imbalance as the sole culprit, we reveal that the trend is closely associated with an effect we call source signal dilution, where strong lexical cues for the new symbol become diluted as the training dataset grows.
ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question Answering
Question answering on knowledge bases (KBQA) poses a unique challenge for semantic parsing research due to two intertwined challenges: large search space and ambiguities in schema linking.
Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again
In this paper, we present the first systematic and comprehensive study to compare the few-shot performance of GPT-3 in-context learning with fine-tuning smaller (i. e., BERT-sized) PLMs on two highly representative biomedical information extraction tasks, named entity recognition and relation extraction.
One Step at a Time: Long-Horizon Vision-and-Language Navigation with Milestones
We study the problem of developing autonomous agents that can follow human instructions to infer and perform a sequence of actions to complete the underlying task.
Compositional Generalization for Natural Language Interfaces to Web APIs
We define new compositional generalization tasks for NL2API which explore the models' ability to extrapolate from simple API calls in the training set to new and more complex API calls in the inference phase.
ReasonBERT: Pre-trained to Reason with Distant Supervision
We present ReasonBert, a pre-training method that augments language models with the ability to reason over long-range relations and multiple, possibly hybrid contexts.
Ranked #1 on
Semantic Parsing
on GraphQuestions
ITNLP at SemEval-2021 Task 11: Boosting BERT with Sampling and Adversarial Training for Knowledge Extraction
This paper describes the winning system in the End-to-end Pipeline phase for the NLPContributionGraph task.
A Systematic Investigation of KB-Text Embedding Alignment at Scale
Knowledge bases (KBs) and text often contain complementary knowledge: KBs store structured knowledge that can support long range reasoning, while text stores more comprehensive and timely knowledge in an unstructured way.
Compositional Generalization for Neural Semantic Parsing via Span-level Supervised Attention
We describe a span-level supervised attention loss that improves compositional generalization in semantic parsers.
Quality meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing
In this paper, we study a novel model-agnostic CAT problem, where we aim to propose a flexible framework that can adapt to different cognitive models.
Explainable Recommendation Systems by Generalized Additive Models with Manifest and Latent Interactions
In this paper, we propose the explainable recommendation systems based on a generalized additive model with manifest and latent interactions (GAMMLI).
An Investigation of Language Model Interpretability via Sentence Editing
Pre-trained language models (PLMs) like BERT are being used for almost all language-related tasks, but interpreting their behavior still remains a significant challenge and many important questions remain largely unanswered.
Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases
To facilitate the development of KBQA models with stronger generalization, we construct and release a new large-scale, high-quality dataset with 64, 331 questions, GrailQA, and provide evaluation settings for all three levels of generalization.
Build Smart Grids on Artificial Intelligence -- A Real-world Example
Power grid data are going big with the deployment of various sensors.
Marcus' electron transfer rate revisited via a Rice-Ramsperger-Kassel-Marcus analogue: A unified formalism for linear and nonlinear solvation scenarios
In this work, on the basis of the thermodynamic solvation potentials analysis, we reexamine Marcus' formula with respect to the Rice-Ramsperger-Kassel-Marcus (RRKM) theory.
Chemical Physics
KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation
We propose a knowledge-grounded pre-training (KGPT), which consists of two parts, 1) a general knowledge-grounded generation model to generate knowledge-enriched text.
Ranked #9 on
KG-to-Text Generation
on WebNLG 2.0 (Unconstrained)
Task-Oriented Dialogue as Dataflow Synthesis
We describe an approach to task-oriented dialogue in which dialogue state is represented as a dataflow graph.
Document Classification for COVID-19 Literature
The global pandemic has made it more important than ever to quickly and accurately retrieve relevant scientific literature for effective consumption by researchers in a wide range of fields.
An Imitation Game for Learning Semantic Parsers from User Interaction
Despite the widely successful applications, bootstrapping and fine-tuning semantic parsers are still a tedious process with challenges such as costly data annotation and privacy risks.
Logical Natural Language Generation from Open-Domain Tables
To facilitate the study of the proposed logical NLG problem, we use the existing TabFact dataset \cite{chen2019tabfact} featured with a wide range of logical/symbolic inferences as our testbed, and propose new automatic metrics to evaluate the fidelity of generation models w. r. t.\ logical inference.
Decision Propagation Networks for Image Classification
High-level (e. g., semantic) features encoded in the latter layers of convolutional neural networks are extensively exploited for image classification, leaving low-level (e. g., color) features in the early layers underexplored.
Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Study
As a promising paradigm, interactive semantic parsing has shown to improve both semantic parsing accuracy and user confidence in the results.
EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction
In EERNN, we simply summarize each student's state into an integrated vector and trace it with a recurrent neural network, where we design a bidirectional LSTM to learn the encoding of each exercise's content.
Global Textual Relation Embedding for Relational Understanding
Pre-trained embeddings such as word embeddings and sentence embeddings are fundamental tools facilitating a wide range of downstream NLP tasks.
Ranked #120 on
Action Classification
on Kinetics-400
QuesNet: A Unified Representation for Heterogeneous Test Questions
Then we propose a two-level hierarchical pre-training algorithm to learn better understanding of test questions in an unsupervised way.
How Large a Vocabulary Does Text Classification Need? A Variational Approach to Vocabulary Selection
Under deep neural networks, a pre-defined vocabulary is required to vectorize text inputs.
Learning to Compose Topic-Aware Mixture of Experts for Zero-Shot Video Captioning
Although promising results have been achieved in video captioning, existing models are limited to the fixed inventory of activities in the training corpus, and do not generalize to open vocabulary scenarios.
What It Takes to Achieve 100\% Condition Accuracy on WikiSQL
The SQL queries in WikiSQL are simple: Each involves one relation and does not have any join operation.
XL-NBT: A Cross-lingual Neural Belief Tracking Framework
Then, we pre-train a state tracker for the source language as a teacher, which is able to exploit easy-to-access parallel data.
DialSQL: Dialogue Based Structured Query Generation
The recent advance in deep learning and semantic parsing has significantly improved the translation accuracy of natural language questions to structured queries.
Aggregated Channels Network for Real-Time Pedestrian Detection
Convolutional neural networks (CNNs) have demonstrated their superiority in numerous computer vision tasks, yet their computational cost results prohibitive for many real-time applications such as pedestrian detection which is usually performed on low-consumption hardware.
Recovering Question Answering Errors via Query Revision
The existing factoid QA systems often lack a post-inspection component that can help models recover from their own mistakes.
An End-to-End Deep Framework for Answer Triggering with a Novel Group-Level Objective
Given a question and a set of answer candidates, answer triggering determines whether the candidate set contains any correct answers.
Cross-domain Semantic Parsing via Paraphrasing
Existing studies on semantic parsing mainly focus on the in-domain setting.
Global Relation Embedding for Relation Extraction
We study the problem of textual relation embedding with distant supervision.
