Search Results for author: Aditi Chaudhary

Found 19 papers, 7 papers with code

Teacher Perception of Automatically Extracted Grammar Concepts for L2 Language Learning

no code implementations10 Jun 2022 Aditi Chaudhary, Arun Sampath, Ashwin Sheshadri, Antonios Anastasopoulos, Graham Neubig

This process is challenging because i) it requires that such experts be accessible and have the necessary resources, and ii) even if there are such experts, describing all the intricacies of a language is time-consuming and prone to omission.

AUTOLEX: An Automatic Framework for Linguistic Exploration

no code implementations25 Mar 2022 Aditi Chaudhary, Zaid Sheikh, David R Mortensen, Antonios Anastasopoulos, Graham Neubig

Each language has its own complex systems of word, phrase, and sentence construction, the guiding principles of which are often summarized in grammar descriptions for the consumption of linguists or language learners.

When is Wall a Pared and when a Muro? -- Extracting Rules Governing Lexical Selection

1 code implementation13 Sep 2021 Aditi Chaudhary, Kayo Yin, Antonios Anastasopoulos, Graham Neubig

Learning fine-grained distinctions between vocabulary items is a key challenge in learning a new language.

Reducing Confusion in Active Learning for Part-Of-Speech Tagging

no code implementations2 Nov 2020 Aditi Chaudhary, Antonios Anastasopoulos, Zaid Sheikh, Graham Neubig

Active learning (AL) uses a data selection algorithm to select useful training samples to minimize annotation cost.

Active Learning Part-Of-Speech Tagging +1

DICT-MLM: Improved Multilingual Pre-Training using Bilingual Dictionaries

no code implementations23 Oct 2020 Aditi Chaudhary, Karthik Raman, Krishna Srinivasan, Jiecao Chen

In particular, by requiring the model to predict the language-specific token, the MLM objective disincentivizes learning a language-agnostic representation -- which is a key goal of multilingual pre-training.

Language Modelling Masked Language Modeling +2

Automatic Extraction of Rules Governing Morphological Agreement

1 code implementation EMNLP 2020 Aditi Chaudhary, Antonios Anastasopoulos, Adithya Pratapa, David R. Mortensen, Zaid Sheikh, Yulia Tsvetkov, Graham Neubig

Using cross-lingual transfer, even with no expert annotations in the language of interest, our framework extracts a grammatical specification which is nearly equivalent to those created with large amounts of gold-standard annotated data.

Cross-Lingual Transfer

Exploring Neural Architectures And Techniques For Typologically Diverse Morphological Inflection

no code implementations WS 2020 Pratik Jayarao, Siddhanth Pillay, Pranav Thombre, Aditi Chaudhary

Morphological inflection in low resource languages is critical to augment existing corpora in Low Resource Languages, which can help develop several applications in these languages with very good social impact.

Morphological Inflection

What A Sunny Day ☔: Toward Emoji-Sensitive Irony Detection

no code implementations WS 2019 Shirley Anugrah Hayati, Aditi Chaudhary, Naoki Otani, Alan W. black

Irony detection is an important task with applications in identification of online abuse and harassment.

A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers

1 code implementation IJCNLP 2019 Aditi Chaudhary, Jiateng Xie, Zaid Sheikh, Graham Neubig, Jaime G. Carbonell

Most state-of-the-art models for named entity recognition (NER) rely on the availability of large amounts of labeled data, making them challenging to extend to new, lower-resourced languages.

Active Learning Cross-Lingual Transfer +3

The ARIEL-CMU Systems for LoReHLT18

no code implementations24 Feb 2019 Aditi Chaudhary, Siddharth Dalmia, Junjie Hu, Xinjian Li, Austin Matthews, Aldrian Obaja Muis, Naoki Otani, Shruti Rijhwani, Zaid Sheikh, Nidhi Vyas, Xinyi Wang, Jiateng Xie, Ruochen Xu, Chunting Zhou, Peter J. Jansen, Yiming Yang, Lori Levin, Florian Metze, Teruko Mitamura, David R. Mortensen, Graham Neubig, Eduard Hovy, Alan W. black, Jaime Carbonell, Graham V. Horwood, Shabnam Tafreshi, Mona Diab, Efsun S. Kayi, Noura Farra, Kathleen McKeown

This paper describes the ARIEL-CMU submissions to the Low Resource Human Language Technologies (LoReHLT) 2018 evaluations for the tasks Machine Translation (MT), Entity Discovery and Linking (EDL), and detection of Situation Frames in Text and Speech (SF Text and Speech).

Machine Translation Translation

Weighted Global Normalization for Multiple Choice Reading Comprehension over Long Documents

no code implementations5 Dec 2018 Aditi Chaudhary, Bhargavi Paranjape, Michiel de Jong

Motivated by recent evidence pointing out the fragility of high-performing span prediction models, we direct our attention to multiple choice reading comprehension.

Answer Selection Multiple-choice +1

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