Search Results for author: Raymond J. Mooney

Found 28 papers, 8 papers with code

Evaluation Methodologies for Code Learning Tasks

no code implementations22 Aug 2021 Pengyu Nie, Jiyang Zhang, Junyi Jessy Li, Raymond J. Mooney, Milos Gligoric

This may lead to evaluations that are inconsistent with the intended use cases of the ML models.

Zero-shot Task Adaptation using Natural Language

no code implementations5 Jun 2021 Prasoon Goyal, Raymond J. Mooney, Scott Niekum

Imitation learning and instruction-following are two common approaches to communicate a user's intent to a learning agent.

Imitation Learning

Learning to Generate Code Comments from Class Hierarchies

no code implementations24 Mar 2021 Jiyang Zhang, Sheena Panthaplackel, Pengyu Nie, Raymond J. Mooney, Junyi Jessy Li, Milos Gligoric

Descriptive code comments are essential for supporting code comprehension and maintenance.

Deep Just-In-Time Inconsistency Detection Between Comments and Source Code

no code implementations4 Oct 2020 Sheena Panthaplackel, Junyi Jessy Li, Milos Gligoric, Raymond J. Mooney

For extrinsic evaluation, we show the usefulness of our approach by combining it with a comment update model to build a more comprehensive automatic comment maintenance system which can both detect and resolve inconsistent comments based on code changes.

Systematic Generalization on gSCAN with Language Conditioned Embedding

2 code implementations Asian Chapter of the Association for Computational Linguistics 2020 Tong Gao, Qi Huang, Raymond J. Mooney

Systematic Generalization refers to a learning algorithm's ability to extrapolate learned behavior to unseen situations that are distinct but semantically similar to its training data.

Systematic Generalization

PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards

1 code implementation30 Jul 2020 Prasoon Goyal, Scott Niekum, Raymond J. Mooney

Reinforcement learning (RL), particularly in sparse reward settings, often requires prohibitively large numbers of interactions with the environment, thereby limiting its applicability to complex problems.

Improving VQA and its Explanations \\ by Comparing Competing Explanations

no code implementations28 Jun 2020 Jialin Wu, Liyan Chen, Raymond J. Mooney

Most recent state-of-the-art Visual Question Answering (VQA) systems are opaque black boxes that are only trained to fit the answer distribution given the question and visual content.

Question Answering Visual Question Answering

Dialog as a Vehicle for Lifelong Learning

no code implementations26 Jun 2020 Aishwarya Padmakumar, Raymond J. Mooney

Dialog systems research has primarily been focused around two main types of applications - task-oriented dialog systems that learn to use clarification to aid in understanding a goal, and open-ended dialog systems that are expected to carry out unconstrained "chit chat" conversations.

Dialog Policy Learning for Joint Clarification and Active Learning Queries

no code implementations9 Jun 2020 Aishwarya Padmakumar, Raymond J. Mooney

Intelligent systems need to be able to recover from mistakes, resolve uncertainty, and adapt to novel concepts not seen during training.

Active Learning Image Retrieval +1

Learning to Update Natural Language Comments Based on Code Changes

1 code implementation ACL 2020 Sheena Panthaplackel, Pengyu Nie, Milos Gligoric, Junyi Jessy Li, Raymond J. Mooney

We formulate the novel task of automatically updating an existing natural language comment based on changes in the body of code it accompanies.

Associating Natural Language Comment and Source Code Entities

no code implementations13 Dec 2019 Sheena Panthaplackel, Milos Gligoric, Raymond J. Mooney, Junyi Jessy Li

Comments are an integral part of software development; they are natural language descriptions associated with source code elements.

Hidden State Guidance: Improving Image Captioning using An Image Conditioned Autoencoder

no code implementations31 Oct 2019 Jialin Wu, Raymond J. Mooney

Most RNN-based image captioning models receive supervision on the output words to mimic human captions.

Image Captioning

Do Human Rationales Improve Machine Explanations?

no code implementations WS 2019 Julia Strout, Ye Zhang, Raymond J. Mooney

Work on "learning with rationales" shows that humans providing explanations to a machine learning system can improve the system's predictive accuracy.

General Classification Text Classification

Self-Critical Reasoning for Robust Visual Question Answering

1 code implementation NeurIPS 2019 Jialin Wu, Raymond J. Mooney

Visual Question Answering (VQA) deep-learning systems tend to capture superficial statistical correlations in the training data because of strong language priors and fail to generalize to test data with a significantly different question-answer (QA) distribution.

Question Answering Visual Question Answering

Using Natural Language for Reward Shaping in Reinforcement Learning

1 code implementation5 Mar 2019 Prasoon Goyal, Scott Niekum, Raymond J. Mooney

A common approach to reduce interaction time with the environment is to use reward shaping, which involves carefully designing reward functions that provide the agent intermediate rewards for progress towards the goal.

Montezuma's Revenge

Learning a Policy for Opportunistic Active Learning

no code implementations EMNLP 2018 Aishwarya Padmakumar, Peter Stone, Raymond J. Mooney

Active learning identifies data points to label that are expected to be the most useful in improving a supervised model.

Active Learning

Joint Image Captioning and Question Answering

no code implementations22 May 2018 Jialin Wu, Zeyuan Hu, Raymond J. Mooney

Answering visual questions need acquire daily common knowledge and model the semantic connection among different parts in images, which is too difficult for VQA systems to learn from images with the only supervision from answers.

Image Captioning Question Answering +1

Dialog for Language to Code

no code implementations IJCNLP 2017 Shobhit Chaurasia, Raymond J. Mooney

Generating computer code from natural language descriptions has been a long-standing problem.

Code Generation

Leveraging Discourse Information Effectively for Authorship Attribution

1 code implementation IJCNLP 2017 Su Wang, Elisa Ferracane, Raymond J. Mooney

We explore techniques to maximize the effectiveness of discourse information in the task of authorship attribution.

Supervised and Unsupervised Ensembling for Knowledge Base Population

no code implementations16 Apr 2016 Nazneen Fatema Rajani, Raymond J. Mooney

We present results on combining supervised and unsupervised methods to ensemble multiple systems for two popular Knowledge Base Population (KBP) tasks, Cold Start Slot Filling (CSSF) and Tri-lingual Entity Discovery and Linking (TEDL).

Knowledge Base Population Slot Filling

Using Sentence-Level LSTM Language Models for Script Inference

no code implementations ACL 2016 Karl Pichotta, Raymond J. Mooney

There is a small but growing body of research on statistical scripts, models of event sequences that allow probabilistic inference of implicit events from documents.

Representing Meaning with a Combination of Logical and Distributional Models

1 code implementation CL 2016 I. Beltagy, Stephen Roller, Pengxiang Cheng, Katrin Erk, Raymond J. Mooney

In this paper, we focus on the three components of a practical system integrating logical and distributional models: 1) Parsing and task representation is the logic-based part where input problems are represented in probabilistic logic.

Lexical Entailment Natural Language Inference

Training a Multilingual Sportscaster: Using Perceptual Context to Learn Language

no code implementations16 Jan 2014 David L. Chen, Joohyun Kim, Raymond J. Mooney

We present a novel framework for learning to interpret and generate language using only perceptual context as supervision.

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