Search Results for author: Dipanjan Das

Found 46 papers, 19 papers with code

Speech-driven Facial Animation using Cascaded GANs for Learning of Motion and Texture

no code implementations ECCV 2020 Dipanjan Das, Sandika Biswas, Sanjana Sinha, Brojeshwar Bhowmick

Current state-of-the-art methods fail to generate realistic animation from any speech on unknown faces due to their poor gen-eralization over different facial characteristics, languages, and accents.


Exploiting Unfair Advantages: Investigating Opportunistic Trading in the NFT Market

no code implementations5 Sep 2023 Priyanka Bose, Dipanjan Das, Fabio Gritti, Nicola Ruaro, Christopher Kruegel, Giovanni Vigna

Yet, there are sophisticated actors who turn their domain knowledge and market inefficiencies to their strategic advantage; thus extracting value from trades not accessible to others.

Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation

no code implementations28 Apr 2023 Fantine Huot, Joshua Maynez, Shashi Narayan, Reinald Kim Amplayo, Kuzman Ganchev, Annie Louis, Anders Sandholm, Dipanjan Das, Mirella Lapata

While conditional generation models can now generate natural language well enough to create fluent text, it is still difficult to control the generation process, leading to irrelevant, repetitive, and hallucinated content.

Text Generation

QAmeleon: Multilingual QA with Only 5 Examples

1 code implementation15 Nov 2022 Priyanka Agrawal, Chris Alberti, Fantine Huot, Joshua Maynez, Ji Ma, Sebastian Ruder, Kuzman Ganchev, Dipanjan Das, Mirella Lapata

The availability of large, high-quality datasets has been one of the main drivers of recent progress in question answering (QA).

Few-Shot Learning Question Answering

Query Refinement Prompts for Closed-Book Long-Form Question Answering

no code implementations31 Oct 2022 Reinald Kim Amplayo, Kellie Webster, Michael Collins, Dipanjan Das, Shashi Narayan

Large language models (LLMs) have been shown to perform well in answering questions and in producing long-form texts, both in few-shot closed-book settings.

Long Form Question Answering

Language Models are Multilingual Chain-of-Thought Reasoners

2 code implementations6 Oct 2022 Freda Shi, Mirac Suzgun, Markus Freitag, Xuezhi Wang, Suraj Srivats, Soroush Vosoughi, Hyung Won Chung, Yi Tay, Sebastian Ruder, Denny Zhou, Dipanjan Das, Jason Wei

Finally, we show that the multilingual reasoning abilities of language models extend to other tasks such as commonsense reasoning and word-in-context semantic judgment.

GSM8K Math

Conditional Generation with a Question-Answering Blueprint

1 code implementation1 Jul 2022 Shashi Narayan, Joshua Maynez, Reinald Kim Amplayo, Kuzman Ganchev, Annie Louis, Fantine Huot, Anders Sandholm, Dipanjan Das, Mirella Lapata

The ability to convey relevant and faithful information is critical for many tasks in conditional generation and yet remains elusive for neural seq-to-seq models whose outputs often reveal hallucinations and fail to correctly cover important details.

Question Answering Question Generation +1

A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation

1 code implementation ACL 2022 Shashi Narayan, Gonçalo Simões, Yao Zhao, Joshua Maynez, Dipanjan Das, Michael Collins, Mirella Lapata

We propose Composition Sampling, a simple but effective method to generate diverse outputs for conditional generation of higher quality compared to previous stochastic decoding strategies.

Question Generation Question-Generation

Measuring Attribution in Natural Language Generation Models

1 code implementation23 Dec 2021 Hannah Rashkin, Vitaly Nikolaev, Matthew Lamm, Lora Aroyo, Michael Collins, Dipanjan Das, Slav Petrov, Gaurav Singh Tomar, Iulia Turc, David Reitter

With recent improvements in natural language generation (NLG) models for various applications, it has become imperative to have the means to identify and evaluate whether NLG output is only sharing verifiable information about the external world.

Text Generation

Decontextualization: Making Sentences Stand-Alone

no code implementations9 Feb 2021 Eunsol Choi, Jennimaria Palomaki, Matthew Lamm, Tom Kwiatkowski, Dipanjan Das, Michael Collins

Models for question answering, dialogue agents, and summarization often interpret the meaning of a sentence in a rich context and use that meaning in a new context.

document understanding Question Answering

Variational Clustering: Leveraging Variational Autoencoders for Image Clustering

no code implementations10 May 2020 Vignesh Prasad, Dipanjan Das, Brojeshwar Bhowmick

Since we wish to efficiently discriminate between different clusters in the data, we propose a method based on VAEs where we use a Gaussian Mixture prior to help cluster the images accurately.

Clustering Image Clustering

ToTTo: A Controlled Table-To-Text Generation Dataset

1 code implementation EMNLP 2020 Ankur P. Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, Dipanjan Das

We present ToTTo, an open-domain English table-to-text dataset with over 120, 000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.

Conditional Text Generation Data-to-Text Generation +1

Syntactic Data Augmentation Increases Robustness to Inference Heuristics

1 code implementation ACL 2020 Junghyun Min, R. Thomas McCoy, Dipanjan Das, Emily Pitler, Tal Linzen

Pretrained neural models such as BERT, when fine-tuned to perform natural language inference (NLI), often show high accuracy on standard datasets, but display a surprising lack of sensitivity to word order on controlled challenge sets.

Data Augmentation Natural Language Inference

BLEURT: Learning Robust Metrics for Text Generation

3 code implementations ACL 2020 Thibault Sellam, Dipanjan Das, Ankur P. Parikh

We propose BLEURT, a learned evaluation metric based on BERT that can model human judgments with a few thousand possibly biased training examples.

Text Generation

Handling Divergent Reference Texts when Evaluating Table-to-Text Generation

1 code implementation ACL 2019 Bhuwan Dhingra, Manaal Faruqui, Ankur Parikh, Ming-Wei Chang, Dipanjan Das, William W. Cohen

Automatically constructed datasets for generating text from semi-structured data (tables), such as WikiBio, often contain reference texts that diverge from the information in the corresponding semi-structured data.

Table-to-Text Generation

BERT Rediscovers the Classical NLP Pipeline

1 code implementation ACL 2019 Ian Tenney, Dipanjan Das, Ellie Pavlick

Pre-trained text encoders have rapidly advanced the state of the art on many NLP tasks.


Deep Representation Learning Characterized by Inter-class Separation for Image Clustering

no code implementations19 Jan 2019 Dipanjan Das, Ratul Ghosh, Brojeshwar Bhowmick

Despite significant advances in clustering methods in recent years, the outcome of clustering of a natural image dataset is still unsatisfactory due to two important drawbacks.

Clustering Image Clustering +1

Epipolar Geometry based Learning of Multi-view Depth and Ego-Motion from Monocular Sequences

no code implementations23 Dec 2018 Vignesh Prasad, Dipanjan Das, Brojeshwar Bhowmick

The proposed method results in better depth images and pose estimates, which capture the scene structure and motion in a better way.

Visual Odometry

Identifying Well-formed Natural Language Questions

1 code implementation EMNLP 2018 Manaal Faruqui, Dipanjan Das

Understanding search queries is a hard problem as it involves dealing with "word salad" text ubiquitously issued by users.

Query Wellformedness Test

Learning To Split and Rephrase From Wikipedia Edit History

1 code implementation EMNLP 2018 Jan A. Botha, Manaal Faruqui, John Alex, Jason Baldridge, Dipanjan Das

Split and rephrase is the task of breaking down a sentence into shorter ones that together convey the same meaning.

Split and Rephrase

Learning Recurrent Span Representations for Extractive Question Answering

1 code implementation4 Nov 2016 Kenton Lee, Shimi Salant, Tom Kwiatkowski, Ankur Parikh, Dipanjan Das, Jonathan Berant

In this paper, we focus on this answer extraction task, presenting a novel model architecture that efficiently builds fixed length representations of all spans in the evidence document with a recurrent network.

Answer Selection Extractive Question-Answering +2

Transforming Dependency Structures to Logical Forms for Semantic Parsing

1 code implementation TACL 2016 Siva Reddy, Oscar T{\"a}ckstr{\"o}m, Michael Collins, Tom Kwiatkowski, Dipanjan Das, Mark Steedman, Mirella Lapata

In contrast{---}partly due to the lack of a strong type system{---}dependency structures are easy to annotate and have become a widely used form of syntactic analysis for many languages.

Question Answering Semantic Parsing +1

A Universal Part-of-Speech Tagset

1 code implementation LREC 2012 Slav Petrov, Dipanjan Das, Ryan Mcdonald

To facilitate future research in unsupervised induction of syntactic structure and to standardize best-practices, we propose a tagset that consists of twelve universal part-of-speech categories.

Cannot find the paper you are looking for? You can Submit a new open access paper.