Search Results for author: Ramakanth Pasunuru

Found 28 papers, 11 papers with code

An Overview of Uncertainty Calibration for Text Classification and the Role of Distillation

no code implementations ACL (RepL4NLP) 2021 Han Guo, Ramakanth Pasunuru, Mohit Bansal

Many recalibration methods have been proposed in the literature for quantifying predictive uncertainty and calibrating model outputs, with varying degrees of complexity.

Pretrained Language Models Text Classification

Continual Few-Shot Learning for Text Classification

1 code implementation EMNLP 2021 Ramakanth Pasunuru, Veselin Stoyanov, Mohit Bansal

In this work, we propose a continual few-shot learning (CFL) task, in which a system is challenged with a difficult phenomenon and asked to learn to correct mistakes with only a few (10 to 15) training examples.

Classification Few-Shot Learning +3

Few-shot Learning with Multilingual Language Models

1 code implementation20 Dec 2021 Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li

In this work, we train multilingual autoregressive language models on a balanced corpus covering a diverse set of languages, and study their few- and zero-shot learning capabilities in a wide range of tasks.

Few-Shot Learning Hate Speech Detection +4

A Proposition-Level Clustering Approach for Multi-Document Summarization

no code implementations16 Dec 2021 Ori Ernst, Avi Caciularu, Ori Shapira, Ramakanth Pasunuru, Mohit Bansal, Jacob Goldberger, Ido Dagan

Text clustering methods were traditionally incorporated into multi-document summarization (MDS) as a means for coping with considerable information repetition.

Document Summarization Multi-Document Summarization +1

Multi-Document Keyphrase Extraction: A Literature Review and the First Dataset

1 code implementation3 Oct 2021 Ori Shapira, Ramakanth Pasunuru, Ido Dagan, Yael Amsterdamer

Keyphrase extraction has been comprehensively researched within the single-document setting, with an abundance of methods and a wealth of datasets.

Keyphrase Extraction

Extending Multi-Document Summarization Evaluation to the Interactive Setting

1 code implementation NAACL 2021 Ori Shapira, Ramakanth Pasunuru, Hadar Ronen, Mohit Bansal, Yael Amsterdamer, Ido Dagan

In this paper, we develop an end-to-end evaluation framework for interactive summarization, focusing on expansion-based interaction, which considers the accumulating information along a user session.

Document Summarization Multi-Document Summarization

Data Augmentation for Abstractive Query-Focused Multi-Document Summarization

1 code implementation2 Mar 2021 Ramakanth Pasunuru, Asli Celikyilmaz, Michel Galley, Chenyan Xiong, Yizhe Zhang, Mohit Bansal, Jianfeng Gao

The progress in Query-focused Multi-Document Summarization (QMDS) has been limited by the lack of sufficient largescale high-quality training datasets.

Data Augmentation Document Summarization +1

Dual Reinforcement-Based Specification Generation for Image De-Rendering

no code implementations2 Mar 2021 Ramakanth Pasunuru, David Rosenberg, Gideon Mann, Mohit Bansal

Since these are sequence models, we must choose an ordering of the objects in the graphics programs for likelihood training.

DORB: Dynamically Optimizing Multiple Rewards with Bandits

no code implementations EMNLP 2020 Ramakanth Pasunuru, Han Guo, Mohit Bansal

Further, it is important to consider using a dynamic combination and curriculum of metric rewards that flexibly changes over time.

Data-to-Text Generation Question Generation

Evaluating Interactive Summarization: an Expansion-Based Framework

no code implementations17 Sep 2020 Ori Shapira, Ramakanth Pasunuru, Hadar Ronen, Mohit Bansal, Yael Amsterdamer, Ido Dagan

Allowing users to interact with multi-document summarizers is a promising direction towards improving and customizing summary results.

Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline

1 code implementation CoNLL (EMNLP) 2021 Ori Ernst, Ori Shapira, Ramakanth Pasunuru, Michael Lepioshkin, Jacob Goldberger, Mohit Bansal, Ido Dagan

Aligning sentences in a reference summary with their counterparts in source documents was shown as a useful auxiliary summarization task, notably for generating training data for salience detection.

Document Summarization Multi-Document Summarization

Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits

no code implementations13 Jan 2020 Han Guo, Ramakanth Pasunuru, Mohit Bansal

Next, we develop a DistanceNet model which uses these distance measures, or a mixture of these distance measures, as an additional loss function to be minimized jointly with the task's loss function, so as to achieve better unsupervised domain adaptation.

Classification General Classification +3

Continual and Multi-Task Architecture Search

1 code implementation ACL 2019 Ramakanth Pasunuru, Mohit Bansal

Architecture search is the process of automatically learning the neural model or cell structure that best suits the given task.

Continual Learning General Classification +5

AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning

no code implementations NAACL 2019 Han Guo, Ramakanth Pasunuru, Mohit Bansal

To address these issues, we present AutoSeM, a two-stage MTL pipeline, where the first stage automatically selects the most useful auxiliary tasks via a Beta-Bernoulli multi-armed bandit with Thompson Sampling, and the second stage learns the training mixing ratio of these selected auxiliary tasks via a Gaussian Process based Bayesian optimization framework.

Multi-Task Learning

Game-Based Video-Context Dialogue

1 code implementation EMNLP 2018 Ramakanth Pasunuru, Mohit Bansal

Current dialogue systems focus more on textual and speech context knowledge and are usually based on two speakers.

Dynamic Multi-Level Multi-Task Learning for Sentence Simplification

no code implementations COLING 2018 Han Guo, Ramakanth Pasunuru, Mohit Bansal

In this work, we first present a strong pointer-copy mechanism based sequence-to-sequence sentence simplification model, and then improve its entailment and paraphrasing capabilities via multi-task learning with related auxiliary tasks of entailment and paraphrase generation.

Multi-Task Learning Paraphrase Generation +1

Multi-Reward Reinforced Summarization with Saliency and Entailment

no code implementations NAACL 2018 Ramakanth Pasunuru, Mohit Bansal

Abstractive text summarization is the task of compressing and rewriting a long document into a short summary while maintaining saliency, directed logical entailment, and non-redundancy.

Abstractive Text Summarization

Towards Improving Abstractive Summarization via Entailment Generation

no code implementations WS 2017 Ramakanth Pasunuru, Han Guo, Mohit Bansal

Abstractive summarization, the task of rewriting and compressing a document into a short summary, has achieved considerable success with neural sequence-to-sequence models.

Abstractive Text Summarization Machine Translation +2

Reinforced Video Captioning with Entailment Rewards

no code implementations EMNLP 2017 Ramakanth Pasunuru, Mohit Bansal

Sequence-to-sequence models have shown promising improvements on the temporal task of video captioning, but they optimize word-level cross-entropy loss during training.

reinforcement-learning Video Captioning

Multi-Task Video Captioning with Video and Entailment Generation

no code implementations ACL 2017 Ramakanth Pasunuru, Mohit Bansal

Video captioning, the task of describing the content of a video, has seen some promising improvements in recent years with sequence-to-sequence models, but accurately learning the temporal and logical dynamics involved in the task still remains a challenge, especially given the lack of sufficient annotated data.

Multi-Task Learning Video Captioning +1

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