no code implementations • ACL 2020 • Md. Arafat Sultan, Ch, Shubham el, Fern, Ram{\'o}n ez Astudillo, Vittorio Castelli
Automatic question generation (QG) has shown promise as a source of synthetic training data for question answering (QA).
no code implementations • LREC 2020 • G{\'a}bor Bella, Fiona McNeill, Rody Gorman, Caoimhin O Donnaile, Kirsty MacDonald, Ch, Yamini rashekar, Abed Alhakim Freihat, Fausto Giunchiglia
We present a new wordnet resource for Scottish Gaelic, a Celtic minority language spoken by about 60, 000 speakers, most of whom live in Northwestern Scotland.
no code implementations • RANLP 2019 • Anush Kumar, Nihal V. Nayak, Ch, Aditya ra, Mydhili K. Nair
Machine Translation systems have drastically improved over the years for several language pairs.
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.
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 • SEMEVAL 2019 • Senthil Kumar B., Thenmozhi D., Ch, Aravindan rabose, Srinethe Sharavanan
We have evaluated our approach on the EmoContext@SemEval2019 dataset and we have obtained the micro-averaged F1 scores as 0. 595 and 0. 6568 for the pre-evaluation dataset and final evaluation test set respectively.
no code implementations • SEMEVAL 2019 • Thenmozhi D., Senthil Kumar B., Srinethe Sharavanan, Ch, Aravindan rabose
Offensive language identification (OLI) in user generated text is automatic detection of any profanity, insult, obscenity, racism or vulgarity that degrades an individual or a group.
no code implementations • WS 2019 • Ch, Chelsea ler, Peter W. Foltz, Jian Cheng, Jared C. Bernstein, Elizabeth P. Rosenfeld, Alex S. Cohen, Terje B. Holmlund, Brita Elvev{\aa}g
A final set of three features were used to both predict expert human ratings with a ridge regression model (r = 0. 88) and to differentiate patients from healthy individuals with an ensemble of logistic regression classifiers (accuracy = 76{\%}).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
no code implementations • TACL 2019 • Ch, Jane lee, Adam Jardine
Focusing on the domain of tone, we investigate this ability of ARs using a computationally well-defined notion of locality extended from Chandlee (2014).
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 • 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, 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 • 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 • Niyati Chhaya, Kushal Chawla, Tanya Goyal, Ch, Projjal a, Jaya Singh
We present a novel approach to model human frustration in text.
no code implementations • EMNLP 2017 • Caglar Gulcehre, Ch, Sarath ar
We will present a unified architecture for Memory Augmented Neural Networks (MANN) and discuss the ways in which one can address the external memory and hence read/write from it.
no code implementations • EMNLP 2017 • Daniel Preo{\c{t}}iuc-Pietro, Ch, Sharath ra Guntuku, Lyle Ungar
Much of our online communication is text-mediated and, lately, more common with automated agents.
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 • NAACL 2016 • Paul Crook, Alex Marin, Vipul Agarwal, Khushboo Aggarwal, Tasos Anastasakos, Ravi Bikkula, Daniel Boies, Asli Celikyilmaz, Ch, Senthilkumar ramohan, Zhaleh Feizollahi, Roman Holenstein, Minwoo Jeong, Omar Khan, Young-Bum Kim, Elizabeth Krawczyk, Xiaohu Liu, Danko Panic, Vasiliy Radostev, Nikhil Ramesh, Jean-Phillipe Robichaud, Alex Rochette, re, Logan Stromberg, Ruhi Sarikaya
no code implementations • TACL 2014 • Ch, Jane lee, R{\'e}mi Eyraud, Jeffrey Heinz
We provide an automata-theoretic characterization of the ISL class and theorems establishing how the classes are related to each other and to Strictly Local languages.