Karteek Addanki


2014

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Evaluating Improvised Hip Hop Lyrics - Challenges and Observations
Karteek Addanki | Dekai Wu
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We investigate novel challenges involved in comparing model performance on the task of improvising responses to hip hop lyrics and discuss observations regarding inter-evaluator agreement on judging improvisation quality. We believe the analysis serves as a first step toward designing robust evaluation strategies for improvisation tasks, a relatively neglected area to date. Unlike most natural language processing tasks, improvisation tasks suffer from a high degree of subjectivity, making it difficult to design discriminative evaluation strategies to drive model development. We propose a simple strategy with fluency and rhyming as the criteria for evaluating the quality of generated responses, which we apply to both our inversion transduction grammar based FREESTYLE hip hop challenge-response improvisation system, as well as various contrastive systems. We report inter-evaluator agreement for both English and French hip hop lyrics, and analyze correlation with challenge length. We also compare the extent of agreement in evaluating fluency with that of rhyming, and quantify the difference in agreement with and without precise definitions of evaluation criteria.

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Transduction Recursive Auto-Associative Memory: Learning Bilingual Compositional Distributed Vector Representations of Inversion Transduction Grammars
Karteek Addanki | Dekai Wu
Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation

2013

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Learning to Freestyle: Hip Hop Challenge-Response Induction via Transduction Rule Segmentation
Dekai Wu | Karteek Addanki | Markus Saers | Meriem Beloucif
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Modeling Hip Hop Challenge-Response Lyrics as Machine Translation
Karteek Addanki | Markus Saers | Dekai Wu
Proceedings of Machine Translation Summit XIV: Papers

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Segmenting vs. Chunking Rules: Unsupervised ITG Induction via Minimum Conditional Description Length
Markus Saers | Karteek Addanki | Dekai Wu
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

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Combining Top-down and Bottom-up Search for Unsupervised Induction of Transduction Grammars
Markus Saers | Karteek Addanki | Dekai Wu
Proceedings of the Seventh Workshop on Syntax, Semantics and Structure in Statistical Translation

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Unsupervised Transduction Grammar Induction via Minimum Description Length
Markus Saers | Karteek Addanki | Dekai Wu
Proceedings of the Second Workshop on Hybrid Approaches to Translation

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Improving machine translation by training against an automatic semantic frame based evaluation metric
Chi-kiu Lo | Karteek Addanki | Markus Saers | Dekai Wu
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2012

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From Finite-State to Inversion Transductions: Toward Unsupervised Bilingual Grammar Induction
Markus Saers | Karteek Addanki | Dekai Wu
Proceedings of COLING 2012

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LTG vs. ITG Coverage of Cross-Lingual Verb Frame Alternations
Karteek Addanki | Chi-kiu Lo | Markus Saers | Dekai Wu
Proceedings of the 16th Annual Conference of the European Association for Machine Translation