10 citations · 20 across the 7 of their papers we have counts for
11 papers
M3T: A New Benchmark Dataset for Multi-Modal Document-Level Machine Translation
Benjamin Hsu, Xiaoyu Liu, Huayang Li +6
Document translation poses a challenge for Neural Machine Translation (NMT) systems. Most document-level NMT systems rely on meticulously curated sentence-level parallel data, assu…
MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation
Anna Currey, Maria Nădejde, Raghavendra Pappagari +5
As generic machine translation (MT) quality has improved, the need for targeted benchmarks that explore fine-grained aspects of quality has increased. In particular, gender accurac…
Non-Contrastive Self-Supervised Learning of Utterance-Level Speech Representations
Jaejin Cho, Raghavendra Pappagari, Piotr Żelasko +3
Considering the abundance of unlabeled speech data and the high labeling costs, unsupervised learning methods can be essential for better system development. One of the most succes…
Beyond Isolated Utterances: Conversational Emotion Recognition
Raghavendra Pappagari, Piotr Żelasko, Jesús Villalba +2
Speech emotion recognition is the task of recognizing the speaker's emotional state given a recording of their utterance. While most of the current approaches focus on inferring em…
Joint prediction of truecasing and punctuation for conversational speech in low-resource scenarios
Raghavendra Pappagari, Piotr Żelasko, Agnieszka Mikołajczyk +2
Capitalization and punctuation are important cues for comprehending written texts and conversational transcripts. Yet, many ASR systems do not produce punctuated and case-formatted…
What Helps Transformers Recognize Conversational Structure? Importance of Context, Punctuation, and Labels in Dialog Act Recognition
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. The ability to structur…
CopyPaste: An Augmentation Method for Speech Emotion Recognition
Raghavendra Pappagari, Jesús Villalba, Piotr Żelasko +2
Data augmentation is a widely used strategy for training robust machine learning models. It partially alleviates the problem of limited data for tasks like speech emotion recogniti…
x-vectors meet emotions: A study on dependencies between emotion and speaker recognition
Raghavendra Pappagari, Tianzi Wang, Jesus Villalba +2
In this work, we explore the dependencies between speaker recognition and emotion recognition. We first show that knowledge learned for speaker recognition can be reused for emotio…
Deep neural networks for emotion recognition combining audio and transcripts
Jaejin Cho, Raghavendra Pappagari, Purva Kulkarni +3
In this paper, we propose to improve emotion recognition by combining acoustic information and conversation transcripts. On the one hand, an LSTM network was used to detect emotion…
Hierarchical Transformers for Long Document Classification
Raghavendra Pappagari, Piotr Żelasko, Jesús Villalba +2
BERT, which stands for Bidirectional Encoder Representations from Transformers, is a recently introduced language representation model based upon the transfer learning paradigm. We…
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling
Alex Wang, Jan Hula, Patrick Xia +13
Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pre…