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Found 10 papers, 4 papers with code
MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation
As generic machine translation (MT) quality has improved, the need for targeted benchmarks that explore fine-grained aspects of quality has increased.
Non-Contrastive Self-Supervised Learning of Utterance-Level Speech Representations
This paper applies a non-contrastive self-supervised learning method on an unlabeled speech corpus to learn utterance-level embeddings.
Joint prediction of truecasing and punctuation for conversational speech in low-resource scenarios
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.
Beyond Isolated Utterances: Conversational Emotion Recognition
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.
What Helps Transformers Recognize Conversational Structure? Importance of Context, Punctuation, and Labels in Dialog Act Recognition
Dialog acts can be interpreted as the atomic units of a conversation, more fine-grained than utterances, characterized by a specific communicative function.
CopyPaste: An Augmentation Method for Speech Emotion Recognition
Data augmentation is a widely used strategy for training robust machine learning models.
x-vectors meet emotions: A study on dependencies between emotion and speaker recognition
Then, we show the effect of emotion on speaker recognition.
Hierarchical Transformers for Long Document Classification
BERT, which stands for Bidirectional Encoder Representations from Transformers, is a recently introduced language representation model based upon the transfer learning paradigm.
Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling
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).
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling
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 pretrained on variants of language modeling.
