no code implementations • WS 2017 • Khyathi u, Aakanksha Naik, Ch, Aditya rasekar, Zi Yang, Niloy Gupta, Eric Nyberg
In this paper, we describe our participation in phase B of task 5b of the fifth edition of the annual BioASQ challenge, which includes answering factoid, list, yes-no and summary questions from biomedical data.
no code implementations • WS 2018 • Ch, Khyathi u, Ekaterina Loginova, Vishal Gupta, Josef van Genabith, G{\"u}nter Neumann, Manoj Chinnakotla, Eric Nyberg, Alan W. black
As a first step towards fostering research which supports CM in NLP applications, we systematically crowd-sourced and curated an evaluation dataset for factoid question answering in three CM languages - Hinglish (Hindi+English), Tenglish (Telugu+English) and Tamlish (Tamil+English) which belong to two language families (Indo-Aryan and Dravidian).
no code implementations • WS 2018 • Parvathy Geetha, Ch, Khyathi u, Alan W. black
In this paper we describe models that intuitively developed from the data for the shared task Named Entity Recognition on Code-switched Data.
no code implementations • WS 2018 • Ch, Khyathi u, Thomas Manzini, Sumeet Singh, Alan W. black
Code-switching (CS), the practice of alternating between two or more languages in conversations, is pervasive in most multi-lingual communities.
no code implementations • WS 2018 • Yutong Li, Nicholas Gekakis, Qiuze Wu, Boyue Li, Ch, Khyathi u, Eric Nyberg
The growing number of biomedical publications is a challenge for human researchers, who invest considerable effort to search for relevant documents and pinpointed answers.
no code implementations • WS 2018 • Ashwin Naresh Kumar, Harini Kesavamoorthy, Madhura Das, Pramati Kalwad, Ch, Khyathi u, Teruko Mitamura, Eric Nyberg
The ever-increasing magnitude of biomedical information sources makes it difficult and time-consuming for a human researcher to find the most relevant documents and pinpointed answers for a specific question or topic when using only a traditional search engine.
no code implementations • ICLR Workshop DeepGenStruct 2019 • Ch, Khyathi u, Eric Nyberg, Alan W. black
We introduce a dataset for sequential procedural (how-to) text generation from images in cooking domain.
no code implementations • WS 2019 • Ch, Khyathi u, Shrimai Prabhumoye, Ruslan Salakhutdinov, Alan W. black
To this end, we propose five models which are incremental extensions to the baseline model to perform the task at hand.