Search Results for author: Marjan Ghazvininejad

Found 39 papers, 14 papers with code

Non-autoregressive Translation with Disentangled Context Transformer

1 code implementation ICML 2020 Jungo Kasai, James Cross, Marjan Ghazvininejad, Jiatao Gu

State-of-the-art neural machine translation models generate a translation from left to right and every step is conditioned on the previously generated tokens.

Machine Translation Translation

SSD-2: Scaling and Inference-time Fusion of Diffusion Language Models

no code implementations24 May 2023 Xiaochuang Han, Sachin Kumar, Yulia Tsvetkov, Marjan Ghazvininejad

Diffusion-based language models (LMs) have been shown to be competent generative models that are easy to control at inference and are a promising alternative to autoregressive LMs.

Dictionary-based Phrase-level Prompting of Large Language Models for Machine Translation

no code implementations15 Feb 2023 Marjan Ghazvininejad, Hila Gonen, Luke Zettlemoyer

Large language models (LLMs) demonstrate remarkable machine translation (MT) abilities via prompting, even though they were not explicitly trained for this task.

Machine Translation Translation

Representation Deficiency in Masked Language Modeling

no code implementations4 Feb 2023 Yu Meng, Jitin Krishnan, Sinong Wang, Qifan Wang, Yuning Mao, Han Fang, Marjan Ghazvininejad, Jiawei Han, Luke Zettlemoyer

In this work, we offer a new perspective on the consequence of such a discrepancy: We demonstrate empirically and theoretically that MLM pretraining allocates some model dimensions exclusively for representing $\texttt{[MASK]}$ tokens, resulting in a representation deficiency for real tokens and limiting the pretrained model's expressiveness when it is adapted to downstream data without $\texttt{[MASK]}$ tokens.

Language Modelling Masked Language Modeling

In-context Examples Selection for Machine Translation

no code implementations5 Dec 2022 Sweta Agrawal, Chunting Zhou, Mike Lewis, Luke Zettlemoyer, Marjan Ghazvininejad

Large-scale generative models show an impressive ability to perform a wide range of Natural Language Processing (NLP) tasks using in-context learning, where a few examples are used to describe a task to the model.

Language Modelling Machine Translation +1

Natural Language to Code Translation with Execution

1 code implementation25 Apr 2022 Freda Shi, Daniel Fried, Marjan Ghazvininejad, Luke Zettlemoyer, Sida I. Wang

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.

Code Translation Translation

A Review on Language Models as Knowledge Bases

no code implementations12 Apr 2022 Badr AlKhamissi, Millicent Li, Asli Celikyilmaz, Mona Diab, Marjan Ghazvininejad

Recently, there has been a surge of interest in the NLP community on the use of pretrained Language Models (LMs) as Knowledge Bases (KBs).

Discourse-Aware Soft Prompting for Text Generation

no code implementations10 Dec 2021 Marjan Ghazvininejad, Vladimir Karpukhin, Vera Gor, Asli Celikyilmaz

We show that soft-prompt based conditional text generation can be improved with simple and efficient methods that simulate modeling the discourse structure of human written text.

Conditional Text Generation Language Modelling

Distributionally Robust Multilingual Machine Translation

1 code implementation EMNLP 2021 Chunting Zhou, Daniel Levy, Xian Li, Marjan Ghazvininejad, Graham Neubig

Multilingual neural machine translation (MNMT) learns to translate multiple language pairs with a single model, potentially improving both the accuracy and the memory-efficiency of deployed models.

Machine Translation Translation

Prompting Contrastive Explanations for Commonsense Reasoning Tasks

no code implementations Findings (ACL) 2021 Bhargavi Paranjape, Julian Michael, Marjan Ghazvininejad, Luke Zettlemoyer, Hannaneh Hajishirzi

Many commonsense reasoning NLP tasks involve choosing between one or more possible answers to a question or prompt based on knowledge that is often implicit.

EASE: Extractive-Abstractive Summarization with Explanations

no code implementations14 May 2021 Haoran Li, Arash Einolghozati, Srinivasan Iyer, Bhargavi Paranjape, Yashar Mehdad, Sonal Gupta, Marjan Ghazvininejad

Current abstractive summarization systems outperform their extractive counterparts, but their widespread adoption is inhibited by the inherent lack of interpretability.

Abstractive Text Summarization Document Summarization +1

Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog

1 code implementation NAACL 2021 Arun Babu, Akshat Shrivastava, Armen Aghajanyan, Ahmed Aly, Angela Fan, Marjan Ghazvininejad

Semantic parsing using sequence-to-sequence models allows parsing of deeper representations compared to traditional word tagging based models.

Semantic Parsing

Detecting Hallucinated Content in Conditional Neural Sequence Generation

2 code implementations Findings (ACL) 2021 Chunting Zhou, Graham Neubig, Jiatao Gu, Mona Diab, Paco Guzman, Luke Zettlemoyer, Marjan Ghazvininejad

Neural sequence models can generate highly fluent sentences, but recent studies have also shown that they are also prone to hallucinate additional content not supported by the input.

Abstractive Text Summarization Machine Translation

DeLighT: Deep and Light-weight Transformer

2 code implementations ICLR 2021 Sachin Mehta, Marjan Ghazvininejad, Srinivasan Iyer, Luke Zettlemoyer, Hannaneh Hajishirzi

We introduce a deep and light-weight transformer, DeLighT, that delivers similar or better performance than standard transformer-based models with significantly fewer parameters.

Language Modelling Machine Translation +1

Simple and Effective Retrieve-Edit-Rerank Text Generation

no code implementations ACL 2020 Nabil Hossain, Marjan Ghazvininejad, Luke Zettlemoyer

Retrieve-and-edit seq2seq methods typically retrieve an output from the training set and learn a model to edit it to produce the final output.

Machine Translation Re-Ranking +2

Pre-training via Paraphrasing

2 code implementations NeurIPS 2020 Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh, Armen Aghajanyan, Sida Wang, Luke Zettlemoyer

The objective noisily captures aspects of paraphrase, translation, multi-document summarization, and information retrieval, allowing for strong zero-shot performance on several tasks.

Document Summarization Document Translation +6

Aligned Cross Entropy for Non-Autoregressive Machine Translation

1 code implementation ICML 2020 Marjan Ghazvininejad, Vladimir Karpukhin, Luke Zettlemoyer, Omer Levy

This difficultly is compounded during training with cross entropy loss, which can highly penalize small shifts in word order.

Machine Translation Translation

Semi-Autoregressive Training Improves Mask-Predict Decoding

no code implementations23 Jan 2020 Marjan Ghazvininejad, Omer Levy, Luke Zettlemoyer

The recently proposed mask-predict decoding algorithm has narrowed the performance gap between semi-autoregressive machine translation models and the traditional left-to-right approach.

Machine Translation Translation

Multilingual Denoising Pre-training for Neural Machine Translation

5 code implementations22 Jan 2020 Yinhan Liu, Jiatao Gu, Naman Goyal, Xi-An Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer

This paper demonstrates that multilingual denoising pre-training produces significant performance gains across a wide variety of machine translation (MT) tasks.

Denoising Translation +1

Non-Autoregressive Machine Translation with Disentangled Context Transformer

1 code implementation15 Jan 2020 Jungo Kasai, James Cross, Marjan Ghazvininejad, Jiatao Gu

State-of-the-art neural machine translation models generate a translation from left to right and every step is conditioned on the previously generated tokens.

Machine Translation Translation

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

39 code implementations ACL 2020 Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdel-rahman Mohamed, Omer Levy, Ves Stoyanov, Luke Zettlemoyer

We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token.

Abstractive Text Summarization Denoising +5

Towards Controllable Story Generation

no code implementations WS 2018 Nanyun Peng, Marjan Ghazvininejad, Jonathan May, Kevin Knight

We present a general framework of analyzing existing story corpora to generate controllable and creative new stories.

Story Generation

A Knowledge-Grounded Neural Conversation Model

2 code implementations7 Feb 2017 Marjan Ghazvininejad, Chris Brockett, Ming-Wei Chang, Bill Dolan, Jianfeng Gao, Wen-tau Yih, Michel Galley

We generalize the widely-used Seq2Seq approach by conditioning responses on both conversation history and external "facts", allowing the model to be versatile and applicable in an open-domain setting.

Slot Filling

Learning and Optimization with Submodular Functions

no code implementations7 May 2015 Bharath Sankaran, Marjan Ghazvininejad, Xinran He, David Kale, Liron Cohen

Set functions, and specifically submodular set functions, characterize a wide variety of naturally occurring optimization problems, and the property of submodularity of set functions has deep theoretical consequences with wide ranging applications.

From Local Similarity to Global Coding: An Application to Image Classification

no code implementations CVPR 2013 Amirreza Shaban, Hamid R. Rabiee, Mehrdad Farajtabar, Marjan Ghazvininejad

Exploiting the local similarity of a descriptor and its nearby bases, a global measure of association of a descriptor to all the bases is computed.

General Classification Image Classification

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