Adithya Renduchintala


2020

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Machine Translation quality across demographic dialectal variation in Social Media.
Adithya Renduchintala | Dmitriy Genzel
Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)

2019

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Spelling-Aware Construction of Macaronic Texts for Teaching Foreign-Language Vocabulary
Adithya Renduchintala | Philipp Koehn | Jason Eisner
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We present a machine foreign-language teacher that modifies text in a student’s native language (L1) by replacing some word tokens with glosses in a foreign language (L2), in such a way that the student can acquire L2 vocabulary simply by reading the resulting macaronic text. The machine teacher uses no supervised data from human students. Instead, to guide the machine teacher’s choice of which words to replace, we equip a cloze language model with a training procedure that can incrementally learn representations for novel words, and use this model as a proxy for the word guessing and learning ability of real human students. We use Mechanical Turk to evaluate two variants of the student model: (i) one that generates a representation for a novel word using only surrounding context and (ii) an extension that also uses the spelling of the novel word.

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Simple Construction of Mixed-Language Texts for Vocabulary Learning
Adithya Renduchintala | Philipp Koehn | Jason Eisner
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

We present a machine foreign-language teacher that takes documents written in a student’s native language and detects situations where it can replace words with their foreign glosses such that new foreign vocabulary can be learned simply through reading the resulting mixed-language text. We show that it is possible to design such a machine teacher without any supervised data from (human) students. We accomplish this by modifying a cloze language model to incrementally learn new vocabulary items, and use this language model as a proxy for the word guessing and learning ability of real students. Our machine foreign-language teacher decides which subset of words to replace by consulting this language model. We evaluate three variants of our student proxy language models through a study on Amazon Mechanical Turk (MTurk). We find that MTurk “students” were able to guess the meanings of foreign words introduced by the machine teacher with high accuracy for both function words as well as content words in two out of the three models. In addition, we show that students are able to retain their knowledge about the foreign words after they finish reading the document.

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A Call for Prudent Choice of Subword Merge Operations in Neural Machine Translation
Shuoyang Ding | Adithya Renduchintala | Kevin Duh
Proceedings of Machine Translation Summit XVII Volume 1: Research Track

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Character-Aware Decoder for Translation into Morphologically Rich Languages
Adithya Renduchintala | Pamela Shapiro | Kevin Duh | Philipp Koehn
Proceedings of Machine Translation Summit XVII Volume 1: Research Track

2017

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Knowledge Tracing in Sequential Learning of Inflected Vocabulary
Adithya Renduchintala | Philipp Koehn | Jason Eisner
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

We present a feature-rich knowledge tracing method that captures a student’s acquisition and retention of knowledge during a foreign language phrase learning task. We model the student’s behavior as making predictions under a log-linear model, and adopt a neural gating mechanism to model how the student updates their log-linear parameters in response to feedback. The gating mechanism allows the model to learn complex patterns of retention and acquisition for each feature, while the log-linear parameterization results in an interpretable knowledge state. We collect human data and evaluate several versions of the model.

2016

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Analyzing Learner Understanding of Novel L2 Vocabulary
Rebecca Knowles | Adithya Renduchintala | Philipp Koehn | Jason Eisner
Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning

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User Modeling in Language Learning with Macaronic Texts
Adithya Renduchintala | Rebecca Knowles | Philipp Koehn | Jason Eisner
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Creating Interactive Macaronic Interfaces for Language Learning
Adithya Renduchintala | Rebecca Knowles | Philipp Koehn | Jason Eisner
Proceedings of ACL-2016 System Demonstrations