Search Results for author: Dani Yogatama

Found 43 papers, 14 papers with code

Questions Are All You Need to Train a Dense Passage Retriever

no code implementations21 Jun 2022 Devendra Singh Sachan, Mike Lewis, Dani Yogatama, Luke Zettlemoyer, Joelle Pineau, Manzil Zaheer

We introduce ART, a new corpus-level autoencoding approach for training dense retrieval models that does not require any labeled training data.

Denoising Language Modelling

Language Models Can See: Plugging Visual Controls in Text Generation

2 code implementations5 May 2022 Yixuan Su, Tian Lan, Yahui Liu, Fangyu Liu, Dani Yogatama, Yan Wang, Lingpeng Kong, Nigel Collier

MAGIC is a flexible framework and is theoretically compatible with any text generation tasks that incorporate image grounding.

Image Captioning Story Generation +1

HighMMT: Towards Modality and Task Generalization for High-Modality Representation Learning

1 code implementation2 Mar 2022 Paul Pu Liang, Yiwei Lyu, Xiang Fan, Shentong Mo, Dani Yogatama, Louis-Philippe Morency, Ruslan Salakhutdinov

Learning multimodal representations involves discovering correspondences and integrating information from multiple heterogeneous sources of data.

Representation Learning Time Series +1

Relational Memory Augmented Language Models

no code implementations24 Jan 2022 Qi Liu, Dani Yogatama, Phil Blunsom

We present a memory-augmented approach to condition an autoregressive language model on a knowledge graph.

Language Modelling Text Generation

Balancing Average and Worst-case Accuracy in Multitask Learning

no code implementations12 Oct 2021 Paul Michel, Sebastian Ruder, Dani Yogatama

When training and evaluating machine learning models on a large number of tasks, it is important to not only look at average task accuracy -- which may be biased by easy or redundant tasks -- but also worst-case accuracy (i. e. the performance on the task with the lowest accuracy).

Image Classification Language Modelling

Scale Efficiently: Insights from Pretraining and Finetuning Transformers

no code implementations ICLR 2022 Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler

The key findings of this paper are as follows: (1) we show that aside from only the model size, model shape matters for downstream fine-tuning, (2) scaling protocols operate differently at different compute regions, (3) widely adopted T5-base and T5-large sizes are Pareto-inefficient.

Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers

2 code implementations22 Sep 2021 Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler

The key findings of this paper are as follows: (1) we show that aside from only the model size, model shape matters for downstream fine-tuning, (2) scaling protocols operate differently at different compute regions, (3) widely adopted T5-base and T5-large sizes are Pareto-inefficient.

Finetuning Pretrained Transformers into RNNs

1 code implementation EMNLP 2021 Jungo Kasai, Hao Peng, Yizhe Zhang, Dani Yogatama, Gabriel Ilharco, Nikolaos Pappas, Yi Mao, Weizhu Chen, Noah A. Smith

Specifically, we propose a swap-then-finetune procedure: in an off-the-shelf pretrained transformer, we replace the softmax attention with its linear-complexity recurrent alternative and then finetune.

Language Modelling Machine Translation +1

Random Feature Attention

no code implementations ICLR 2021 Hao Peng, Nikolaos Pappas, Dani Yogatama, Roy Schwartz, Noah A. Smith, Lingpeng Kong

RFA can be used as a drop-in replacement for conventional softmax attention and offers a straightforward way of learning with recency bias through an optional gating mechanism.

Language Modelling Machine Translation +2

Adaptive Semiparametric Language Models

no code implementations4 Feb 2021 Dani Yogatama, Cyprien de Masson d'Autume, Lingpeng Kong

We present a language model that combines a large parametric neural network (i. e., a transformer) with a non-parametric episodic memory component in an integrated architecture.

Language Modelling

Mind the Gap: Assessing Temporal Generalization in Neural Language Models

1 code implementation NeurIPS 2021 Angeliki Lazaridou, Adhiguna Kuncoro, Elena Gribovskaya, Devang Agrawal, Adam Liska, Tayfun Terzi, Mai Gimenez, Cyprien de Masson d'Autume, Tomas Kocisky, Sebastian Ruder, Dani Yogatama, Kris Cao, Susannah Young, Phil Blunsom

Hence, given the compilation of ever-larger language modelling datasets, combined with the growing list of language-model-based NLP applications that require up-to-date factual knowledge about the world, we argue that now is the right time to rethink the static way in which we currently train and evaluate our language models, and develop adaptive language models that can remain up-to-date with respect to our ever-changing and non-stationary world.

Language Modelling

Syntactic Structure Distillation Pretraining For Bidirectional Encoders

no code implementations27 May 2020 Adhiguna Kuncoro, Lingpeng Kong, Daniel Fried, Dani Yogatama, Laura Rimell, Chris Dyer, Phil Blunsom

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.

Knowledge Distillation Language Modelling +2

A Call for More Rigor in Unsupervised Cross-lingual Learning

no code implementations ACL 2020 Mikel Artetxe, Sebastian Ruder, Dani Yogatama, Gorka Labaka, Eneko Agirre

We review motivations, definition, approaches, and methodology for unsupervised cross-lingual learning and call for a more rigorous position in each of them.

Cross-Lingual Word Embeddings Translation +2

Modelling Latent Skills for Multitask Language Generation

no code implementations21 Feb 2020 Kris Cao, Dani Yogatama

We show that our latent task variable model outperforms other sequence-to-sequence baselines on average across tasks in the multitask setting.

Few-Shot Learning Text Generation

On the Cross-lingual Transferability of Monolingual Representations

6 code implementations ACL 2020 Mikel Artetxe, Sebastian Ruder, Dani Yogatama

This generalization ability has been attributed to the use of a shared subword vocabulary and joint training across multiple languages giving rise to deep multilingual abstractions.

Cross-Lingual Question Answering Language Modelling +1

A Mutual Information Maximization Perspective of Language Representation Learning

no code implementations ICLR 2020 Lingpeng Kong, Cyprien de Masson d'Autume, Wang Ling, Lei Yu, Zihang Dai, Dani Yogatama

We show state-of-the-art word representation learning methods maximize an objective function that is a lower bound on the mutual information between different parts of a word sequence (i. e., a sentence).

Computer Vision Natural Language Processing +1

Relative Pixel Prediction For Autoregressive Image Generation

no code implementations25 Sep 2019 Wang Ling, Chris Dyer, Lei Yu, Lingpeng Kong, Dani Yogatama, Susannah Young

In natural images, transitions between adjacent pixels tend to be smooth and gradual, a fact that has long been exploited in image compression models based on predictive coding.

Colorization Image Compression +3

Episodic Memory in Lifelong Language Learning

1 code implementation NeurIPS 2019 Cyprien de Masson d'Autume, Sebastian Ruder, Lingpeng Kong, Dani Yogatama

We introduce a lifelong language learning setup where a model needs to learn from a stream of text examples without any dataset identifier.

Continual Learning General Classification +2

Learning and Evaluating General Linguistic Intelligence

no code implementations31 Jan 2019 Dani Yogatama, Cyprien de Masson d'Autume, Jerome Connor, Tomas Kocisky, Mike Chrzanowski, Lingpeng Kong, Angeliki Lazaridou, Wang Ling, Lei Yu, Chris Dyer, Phil Blunsom

We define general linguistic intelligence as the ability to reuse previously acquired knowledge about a language's lexicon, syntax, semantics, and pragmatic conventions to adapt to new tasks quickly.

Natural Language Understanding Question Answering

Variational Smoothing in Recurrent Neural Network Language Models

no code implementations ICLR 2019 Lingpeng Kong, Gabor Melis, Wang Ling, Lei Yu, Dani Yogatama

We present a new theoretical perspective of data noising in recurrent neural network language models (Xie et al., 2017).

Language Modelling

LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better

no code implementations ACL 2018 Adhiguna Kuncoro, Chris Dyer, John Hale, Dani Yogatama, Stephen Clark, Phil Blunsom

Language exhibits hierarchical structure, but recent work using a subject-verb agreement diagnostic argued that state-of-the-art language models, LSTMs, fail to learn long-range syntax sensitive dependencies.

Language Modelling Machine Translation +1

Memory Architectures in Recurrent Neural Network Language Models

no code implementations ICLR 2018 Dani Yogatama, Yishu Miao, Gabor Melis, Wang Ling, Adhiguna Kuncoro, Chris Dyer, Phil Blunsom

We compare and analyze sequential, random access, and stack memory architectures for recurrent neural network language models.

Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems

1 code implementation11 May 2017 Wang Ling, Dani Yogatama, Chris Dyer, Phil Blunsom

Solving algebraic word problems requires executing a series of arithmetic operations---a program---to obtain a final answer.

Program induction

Generative and Discriminative Text Classification with Recurrent Neural Networks

2 code implementations6 Mar 2017 Dani Yogatama, Chris Dyer, Wang Ling, Phil Blunsom

We empirically characterize the performance of discriminative and generative LSTM models for text classification.

Classification Continual Learning +2

Learning to Compose Words into Sentences with Reinforcement Learning

no code implementations28 Nov 2016 Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, Wang Ling

We use reinforcement learning to learn tree-structured neural networks for computing representations of natural language sentences.

reinforcement-learning

Sparse Overcomplete Word Vector Representations

2 code implementations IJCNLP 2015 Manaal Faruqui, Yulia Tsvetkov, Dani Yogatama, Chris Dyer, Noah Smith

Current distributed representations of words show little resemblance to theories of lexical semantics.

Bayesian Optimization of Text Representations

no code implementations EMNLP 2015 Dani Yogatama, Noah A. Smith

When applying machine learning to problems in NLP, there are many choices to make about how to represent input texts.

General Classification Sentiment Analysis +1

Learning Word Representations with Hierarchical Sparse Coding

no code implementations8 Jun 2014 Dani Yogatama, Manaal Faruqui, Chris Dyer, Noah A. Smith

We propose a new method for learning word representations using hierarchical regularization in sparse coding inspired by the linguistic study of word meanings.

Sentence Completion Sentiment Analysis +1

Dynamic Language Models for Streaming Text

no code implementations TACL 2014 Dani Yogatama, Chong Wang, Bryan R. Routledge, Noah A. Smith, Eric P. Xing

We present a probabilistic language model that captures temporal dynamics and conditions on arbitrary non-linguistic context features.

Language Modelling Machine Translation +1

A Sparse and Adaptive Prior for Time-Dependent Model Parameters

no code implementations9 Oct 2013 Dani Yogatama, Bryan R. Routledge, Noah A. Smith

We consider the scenario where the parameters of a probabilistic model are expected to vary over time.

Variational Inference

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