Dzmitry Bahdanau

research

∙ 09/19/2023

In-Context Learning for Text Classification with Many Labels

In-context learning (ICL) using large language models for tasks with man...

0 Aristides Milios, et al. ∙

research

∙ 06/19/2023

RepoFusion: Training Code Models to Understand Your Repository

Despite the huge success of Large Language Models (LLMs) in coding assis...

0 Disha Shrivastava, et al. ∙

research

∙ 05/09/2023

StarCoder: may the source be with you!

The BigCode community, an open-scientific collaboration working on the r...

12 Raymond Li, et al. ∙

research

∙ 01/09/2023

SantaCoder: don't reach for the stars!

The BigCode project is an open-scientific collaboration working on the r...

1 Loubna Ben Allal, et al. ∙

research

∙ 11/20/2022

The Stack: 3 TB of permissively licensed source code

Large Language Models (LLMs) play an ever-increasing role in the field o...

0 Denis Kocetkov, et al. ∙

research

∙ 11/15/2022

On the Compositional Generalization Gap of In-Context Learning

Pretrained large generative language models have shown great performance...

0 Arian Hosseini, et al. ∙

research

∙ 05/19/2022

LAGr: Label Aligned Graphs for Better Systematic Generalization in Semantic Parsing

Semantic parsing is the task of producing structured meaning representat...

0 Dora Jambor, et al. ∙

research

∙ 04/05/2022

Data Augmentation for Intent Classification with Off-the-shelf Large Language Models

Data augmentation is a widely employed technique to alleviate the proble...

13 Gaurav Sahu, et al. ∙

research

∙ 03/15/2022

Evaluating the Text-to-SQL Capabilities of Large Language Models

We perform an empirical evaluation of Text-to-SQL capabilities of the Co...

0 Nitarshan Rajkumar, et al. ∙

research

∙ 12/01/2021

Systematic Generalization with Edge Transformers

Recent research suggests that systematic generalization in natural langu...

0 Leon Bergen, et al. ∙

research

∙ 10/14/2021

LAGr: Labeling Aligned Graphs for Improving Systematic Generalization in Semantic Parsing

Semantic parsing is the task of producing a structured meaning represent...

0 Dora Jambor, et al. ∙

research

∙ 10/13/2021

Compositional Generalization in Dependency Parsing

Compositionality, or the ability to combine familiar units like words in...

0 Emily Goodwin, et al. ∙

research

∙ 09/10/2021

PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models

Large pre-trained language models for textual data have an unconstrained...

0 Torsten Scholak, et al. ∙

research

∙ 05/07/2021

Understanding by Understanding Not: Modeling Negation in Language Models

Negation is a core construction in natural language. Despite being very ...

0 Arian Hosseini, et al. ∙

research

∙ 04/14/2021

Jointly Learning Truth-Conditional Denotations and Groundings using Parallel Attention

We present a model that jointly learns the denotations of words together...

0 Leon Bergen, et al. ∙

research

∙ 10/21/2020

DuoRAT: Towards Simpler Text-to-SQL Models

Recent research has shown that neural text-to-SQL models can effectively...

0 Torsten Scholak, et al. ∙

research

∙ 07/28/2020

Towards Ecologically Valid Research on Language User Interfaces

Language User Interfaces (LUIs) could improve human-machine interaction ...

0 Harm de Vries, et al. ∙

research

∙ 07/24/2020

BabyAI 1.1

The BabyAI platform is designed to measure the sample efficiency of trai...

6 David Yu-Tung Hui, et al. ∙

research

∙ 02/02/2020

Combating False Negatives in Adversarial Imitation Learning

In adversarial imitation learning, a discriminator is trained to differe...

5 Konrad Zolna, et al. ∙

research

∙ 12/12/2019

CLOSURE: Assessing Systematic Generalization of CLEVR Models

The CLEVR dataset of natural-looking questions about 3D-rendered scenes ...

5 Dzmitry Bahdanau, et al. ∙

research

∙ 12/01/2019

Automated curriculum generation for Policy Gradients from Demonstrations

In this paper, we present a technique that improves the process of train...

17 Anirudh Srinivasan, et al. ∙

research

∙ 11/30/2018

Systematic Generalization: What Is Required and Can It Be Learned?

Numerous models for grounded language understanding have been recently p...

6 Dzmitry Bahdanau, et al. ∙

research

∙ 10/18/2018

BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop

Allowing humans to interactively train artificial agents to understand l...

2 Maxime Chevalier-Boisvert, et al. ∙

research

∙ 06/05/2018

Learning to Follow Language Instructions with Adversarial Reward Induction

Recent work has shown that deep reinforcement-learning agents can learn ...

14 Dzmitry Bahdanau, et al. ∙

research

∙ 04/24/2018

Commonsense mining as knowledge base completion? A study on the impact of novelty

Commonsense knowledge bases such as ConceptNet represent knowledge in th...

0 Stanisław Jastrzębski, et al. ∙

research

∙ 06/01/2017

Learning to Compute Word Embeddings On the Fly

Words in natural language follow a Zipfian distribution whereby some wor...

0 Dzmitry Bahdanau, et al. ∙

research

∙ 11/09/2016

Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control

This paper proposes a general method for improving the structure and qua...

0 Natasha Jaques, et al. ∙

research

∙ 05/09/2016

Theano: A Python framework for fast computation of mathematical expressions

Theano is a Python library that allows to define, optimize, and evaluate...

0 The Theano Development Team, et al. ∙

research

∙ 08/18/2015

End-to-End Attention-based Large Vocabulary Speech Recognition

Many of the current state-of-the-art Large Vocabulary Continuous Speech ...

0 Dzmitry Bahdanau, et al. ∙

research

∙ 06/24/2015

Attention-Based Models for Speech Recognition

Recurrent sequence generators conditioned on input data through an atten...

0 Jan Chorowski, et al. ∙

research

∙ 06/01/2015

Blocks and Fuel: Frameworks for deep learning

We introduce two Python frameworks to train neural networks on large dat...

0 Bart van Merriënboer, et al. ∙

research

∙ 12/04/2014

End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Results

We replace the Hidden Markov Model (HMM) which is traditionally used in ...

0 Jan Chorowski, et al. ∙

research

∙ 09/03/2014

Overcoming the Curse of Sentence Length for Neural Machine Translation using Automatic Segmentation

The authors of (Cho et al., 2014a) have shown that the recently introduc...

0 Jean Pouget-Abadie, et al. ∙

research

∙ 09/01/2014

Neural Machine Translation by Jointly Learning to Align and Translate

Neural machine translation is a recently proposed approach to machine tr...

0 Dzmitry Bahdanau, et al. ∙

research

∙ 06/03/2014

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

In this paper, we propose a novel neural network model called RNN Encode...

0 Kyunghyun Cho, et al. ∙

Dzmitry Bahdanau

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