Search Results for author: Ankur Parikh

Found 8 papers, 3 papers with code

Learning with Limited Text Data

no code implementations ACL 2022 Diyi Yang, Ankur Parikh, Colin Raffel

Natural Language Processing (NLP) has achieved great progress in the past decade on the basis of neural models, which often make use of large amounts of labeled data to achieve state-of-the-art performance.

Data Augmentation Structured Prediction +1

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

Improving Span-based Question Answering Systems with Coarsely Labeled Data

no code implementations5 Nov 2018 Hao Cheng, Ming-Wei Chang, Kenton Lee, Ankur Parikh, Michael Collins, Kristina Toutanova

We study approaches to improve fine-grained short answer Question Answering models by integrating coarse-grained data annotated for paragraph-level relevance and show that coarsely annotated data can bring significant performance gains.

Multi-Task Learning Question Answering

Learning Recurrent Span Representations for Extractive Question Answering

2 code implementations4 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 Natural Language Understanding +1

Nonparametric Latent Tree Graphical Models: Inference, Estimation, and Structure Learning

no code implementations16 Jan 2014 Le Song, Han Liu, Ankur Parikh, Eric Xing

Tree structured graphical models are powerful at expressing long range or hierarchical dependency among many variables, and have been widely applied in different areas of computer science and statistics.

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