Search Results for author: Raghavendra Pappagari

Found 11 papers, 5 papers with code

MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation

1 code implementation2 Nov 2022 Anna Currey, Maria Nădejde, Raghavendra Pappagari, Mia Mayer, Stanislas Lauly, Xing Niu, Benjamin Hsu, Georgiana Dinu

As generic machine translation (MT) quality has improved, the need for targeted benchmarks that explore fine-grained aspects of quality has increased.

counterfactual Ethics +3

Joint prediction of truecasing and punctuation for conversational speech in low-resource scenarios

no code implementations13 Sep 2021 Raghavendra Pappagari, Piotr Żelasko, Agnieszka Mikołajczyk, Piotr Pęzik, Najim Dehak

Further, we show that by training the model in the written text domain and then transfer learning to conversations, we can achieve reasonable performance with less data.

Transfer Learning

Beyond Isolated Utterances: Conversational Emotion Recognition

no code implementations13 Sep 2021 Raghavendra Pappagari, Piotr Żelasko, Jesús Villalba, Laureano Moro-Velazquez, Najim Dehak

While most of the current approaches focus on inferring emotion from isolated utterances, we argue that this is not sufficient to achieve conversational emotion recognition (CER) which deals with recognizing emotions in conversations.

Speech Emotion Recognition

What Helps Transformers Recognize Conversational Structure? Importance of Context, Punctuation, and Labels in Dialog Act Recognition

1 code implementation5 Jul 2021 Piotr Żelasko, Raghavendra Pappagari, Najim Dehak

Dialog acts can be interpreted as the atomic units of a conversation, more fine-grained than utterances, characterized by a specific communicative function.

Segmentation Specificity +1

Hierarchical Transformers for Long Document Classification

3 code implementations23 Oct 2019 Raghavendra Pappagari, Piotr Żelasko, Jesús Villalba, Yishay Carmiel, Najim Dehak

BERT, which stands for Bidirectional Encoder Representations from Transformers, is a recently introduced language representation model based upon the transfer learning paradigm.

Classification Document Classification +3

Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling

no code implementations ICLR 2019 Samuel R. Bowman, Ellie Pavlick, Edouard Grave, Benjamin Van Durme, Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen

Work on the problem of contextualized word representation—the development of reusable neural network components for sentence understanding—has recently seen a surge of progress centered on the unsupervised pretraining task of language modeling with methods like ELMo (Peters et al., 2018).

Language Modelling Sentence

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