Search Results for author:
Found 51 papers, 30 papers with code
Analyzing the Language of Food on Social Media
We investigate the predictive power behind the language of food on social media.
Incorporating Both Distributional and Relational Semantics in Word Representations
We investigate the hypothesis that word representations ought to incorporate both distributional and relational semantics.
Incorporating Both Distributional and Relational Semantics in Word Representations
We investigate the hypothesis that word representations ought to incorporate both distributional and relational semantics.
Higher-order Lexical Semantic Models for Non-factoid Answer Reranking
We introduce a higher-order formalism that allows all these lexical semantic models to chain direct evidence to construct indirect associations between question and answer texts, by casting the task as the traversal of graphs that encode direct term associations.
Towards using social media to identify individuals at risk for preventable chronic illness
The strategy uses a game-like quiz with data and questions acquired semi-automatically from Twitter.
Improving Neural Parsing by Disentangling Model Combination and Reranking Effects
Recent work has proposed several generative neural models for constituency parsing that achieve state-of-the-art results.
Ranked #14 on
Constituency Parsing
on Penn Treebank
Effective Inference for Generative Neural Parsing
Generative neural models have recently achieved state-of-the-art results for constituency parsing.
Unified Pragmatic Models for Generating and Following Instructions
We show that explicit pragmatic inference aids in correctly generating and following natural language instructions for complex, sequential tasks.
Speaker-Follower Models for Vision-and-Language Navigation
We use this speaker model to (1) synthesize new instructions for data augmentation and to (2) implement pragmatic reasoning, which evaluates how well candidate action sequences explain an instruction.
Policy Gradient as a Proxy for Dynamic Oracles in Constituency Parsing
Dynamic oracles provide strong supervision for training constituency parsers with exploration, but must be custom defined for a given parser's transition system.
Pragmatically Informative Text Generation
We improve the informativeness of models for conditional text generation using techniques from computational pragmatics.
Ranked #1 on
Data-to-Text Generation
on E2E NLG Challenge
Abstractive Text Summarization
Conditional Text Generation
+3
Are You Looking? Grounding to Multiple Modalities in Vision-and-Language Navigation
The actual grounding can connect language to the environment through multiple modalities, e. g. "stop at the door" might ground into visual objects, while "turn right" might rely only on the geometric structure of a route.
Cross-Domain Generalization of Neural Constituency Parsers
Neural parsers obtain state-of-the-art results on benchmark treebanks for constituency parsing -- but to what degree do they generalize to other domains?
Learning to Segment Actions from Observation and Narration
We apply a generative segmental model of task structure, guided by narration, to action segmentation in video.
Syntactic Structure Distillation Pretraining For Bidirectional Encoders
Textual representation learners trained on large amounts of data have achieved notable success on downstream tasks; intriguingly, they have also performed well on challenging tests of syntactic competence.
Modular Networks for Compositional Instruction Following
In our approach, subgoal modules each carry out natural language instructions for a specific subgoal type.
Reference-Centric Models for Grounded Collaborative Dialogue
We present a grounded neural dialogue model that successfully collaborates with people in a partially-observable reference game.
Inferring Rewards from Language in Context
In classic instruction following, language like "I'd like the JetBlue flight" maps to actions (e. g., selecting that flight).
InCoder: A Generative Model for Code Infilling and Synthesis
Our model is the first generative model that is able to directly perform zero-shot code infilling, which we evaluate on challenging tasks such as type inference, comment generation, and variable re-naming.
Ranked #84 on
Code Generation
on MBPP
Natural Language to Code Translation with Execution
In this work, we introduce execution result--based minimum Bayes risk decoding (MBR-EXEC) for program selection and show that it improves the few-shot performance of pretrained code models on natural-language-to-code tasks.
Ranked #36 on
Code Generation
on MBPP
Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs
We show that one of today's largest language models (GPT-3; Brown et al., 2020) lacks this kind of social intelligence out-of-the box, using two tasks: SocialIQa (Sap et al., 2019), which measures models' ability to understand intents and reactions of participants of social interactions, and ToMi (Le et al., 2019), which measures whether models can infer mental states and realities of participants of situations.
Contrastive Decoding: Open-ended Text Generation as Optimization
We propose contrastive decoding (CD), a reliable decoding approach that optimizes a contrastive objective subject to a plausibility constraint.
Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches
People rely heavily on context to enrich meaning beyond what is literally said, enabling concise but effective communication.
DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation
We introduce DS-1000, a code generation benchmark with a thousand data science problems spanning seven Python libraries, such as NumPy and Pandas.
Human-level play in the game of Diplomacy by combining language models with strategic reasoning
Despite much progress in training AI systems to imitate human language, building agents that use language to communicate intentionally with humans in interactive environments remains a major challenge.
AutoReply: Detecting Nonsense in Dialogue Introspectively with Discriminative Replies
Existing approaches built separate classifiers to detect nonsense in dialogues.
G^3: Geolocation via Guidebook Grounding
We propose the task of Geolocation via Guidebook Grounding that uses a dataset of StreetView images from a diverse set of locations and an associated textual guidebook for GeoGuessr, a popular interactive geolocation game.
Coder Reviewer Reranking for Code Generation
Sampling diverse programs from a code language model and reranking with model likelihood is a popular method for code generation but it is prone to preferring degenerate solutions.
Ranked #22 on
Code Generation
on MBPP
Execution-Based Evaluation for Open-Domain Code Generation
To extend the scope of coding queries to more realistic settings, we propose ODEX, the first Open-Domain EXecution-based natural language (NL) to Python code generation dataset.
SantaCoder: don't reach for the stars!
The BigCode project is an open-scientific collaboration working on the responsible development of large language models for code.
Grounding Language Models to Images for Multimodal Inputs and Outputs
We propose an efficient method to ground pretrained text-only language models to the visual domain, enabling them to process arbitrarily interleaved image-and-text data, and generate text interleaved with retrieved images.
StarCoder: may the source be with you!
The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15. 5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention.
Ranked #42 on
Code Generation
on MBPP
Generating Images with Multimodal Language Models
This mapping network translates hidden representations of text into the embedding space of the visual models, enabling us to leverage the strong text representations of the LLM for visual outputs.
Pragmatic Inference with a CLIP Listener for Contrastive Captioning
We propose a simple yet effective and robust method for contrastive captioning: generating discriminative captions that distinguish target images from very similar alternative distractor images.
WebArena: A Realistic Web Environment for Building Autonomous Agents
Building upon our environment, we release a set of benchmark tasks focusing on evaluating the functional correctness of task completions.
Amortizing Pragmatic Program Synthesis with Rankings
We prove that for a pragmatic synthesizer that uses a single demonstration, our global ranking method exactly replicates RSA's ranked responses.
SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents
We present SOTOPIA, an open-ended environment to simulate complex social interactions between artificial agents and evaluate their social intelligence.
API-Assisted Code Generation for Question Answering on Varied Table Structures
A persistent challenge to table question answering (TableQA) by generating executable programs has been adapting to varied table structures, typically requiring domain-specific logical forms.
Symbolic Planning and Code Generation for Grounded Dialogue
Large language models (LLMs) excel at processing and generating both text and code.
Data Augmentation for Code Translation with Comparable Corpora and Multiple References
One major challenge of translating code between programming languages is that parallel training data is often limited.
Comparative Knowledge Distillation
We observe that prevalent KD techniques and state of the art data augmentation strategies fall short in this constrained setting.
Generating Pragmatic Examples to Train Neural Program Synthesizers
Programming-by-example is the task of synthesizing a program that is consistent with a set of user-provided input-output examples.
Asking More Informative Questions for Grounded Retrieval
When a model is trying to gather information in an interactive setting, it benefits from asking informative questions.
TroVE: Inducing Verifiable and Efficient Toolboxes for Solving Programmatic Tasks
Language models (LMs) can solve tasks such as answering questions about tables or images by writing programs.
VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks
Through extensive quantitative and qualitative analysis, we identify several limitations of text-only LLM agents, and reveal gaps in the capabilities of state-of-the-art multimodal language agents.
Repetition Improves Language Model Embeddings
In this work, we address an architectural limitation of autoregressive models: token embeddings cannot contain information from tokens that appear later in the input.
What Are Tools Anyway? A Survey from the Language Model Perspective
Language models (LMs) are powerful yet mostly for text generation tasks.
CodeBenchGen: Creating Scalable Execution-based Code Generation Benchmarks
We will release the code of both the framework and the dataset upon acceptance.
Dialogue with Robots: Proposals for Broadening Participation and Research in the SLIVAR Community
The ability to interact with machines using natural human language is becoming not just commonplace, but expected.
Interactive Assignments for Teaching Structured Neural NLP
We present a set of assignments for a graduate-level NLP course.
