98 citations · 372 across the 23 of their papers we have counts for
7 papers · 1 filter
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann +122
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained…
Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings
Kalpesh Krishna, Deepak Nathani, Xavier Garcia +2
Style transfer is the task of rewriting a sentence into a target style while approximately preserving content. While most prior literature assumes access to a large style-labelled…
Do Long-Range Language Models Actually Use Long-Range Context?
Simeng Sun, Kalpesh Krishna, Andrew Mattarella-Micke +1
Language models are generally trained on short, truncated input sequences, which limits their ability to use discourse-level information present in long-range context to improve th…
Weakly-Supervised Open-Retrieval Conversational Question Answering
Chen Qu, Liu Yang, Cen Chen +3
Recent studies on Question Answering (QA) and Conversational QA (ConvQA) emphasize the role of retrieval: a system first retrieves evidence from a large collection and then extract…
Hurdles to Progress in Long-form Question Answering
Kalpesh Krishna, Aurko Roy, Mohit Iyyer
The task of long-form question answering (LFQA) involves retrieving documents relevant to a given question and using them to generate a paragraph-length answer. While many models h…
Long Document Summarization in a Low Resource Setting using Pretrained Language Models
Ahsaas Bajaj, Pavitra Dangati, Kalpesh Krishna +7
Abstractive summarization is the task of compressing a long document into a coherent short document while retaining salient information. Modern abstractive summarization methods ar…
An Analysis of Frame-skipping in Reinforcement Learning
Shivaram Kalyanakrishnan, Siddharth Aravindan, Vishwajeet Bagdawat +5
In the practice of sequential decision making, agents are often designed to sense state at regular intervals of time steps, , ignoring state information in between sensi…