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Research Area

Conversational AI

Building software and systems that help people communicate with computers naturally, as if communicating with family and friends.

Publications

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  • Neural Machine Translation (NMT) models are sensitive to small perturbations in the input. Robustness to such perturbations is typically measured using translation quality metrics such as BLEU on the noisy input. This paper proposes additional metrics which measure the relative degradation and changes in translation when small perturbations are added to the input. We focus on a class of models employing
  • Georgios Paraskevopoulos, Srinivas Parthasarathy, Aparna Khare, Shiva Sundaram
    ACL 2020
    2020
    This paper presents an audio visual automatic speech recognition (AV-ASR) system using a Transformer-based architecture. We particularly focus on the scene context provided by the visual information, to ground the ASR. We extract representations for audio features in the encoder layers of the transformer and fuse video features using an additional crossmodal multihead attention layer. Additionally, we incorporate
  • Ashish Khetan, Zohar Karnin
    ACL 2020
    2020
    Transformers (Vaswani et al., 2017) have gradually become a key component for many state-of-the-art natural-language-representation models. A recent Transformer-based model — BERT (Devlin et al., 2018) — achieved state-of-the-art results on various natural-language-processing tasks, including GLUE, SQuAD v1.1, and SQuAD v2.0. This model however is computationally prohibitive and has a huge number of parameters
  • Luca Soldaini, Alessandro Moschitti
    ACL 2020
    2020
    Large transformer-based language models have been shown to be very effective in many classification tasks. However, their computational complexity prevents their use in applications requiring the classification of a large set of candidates. While previous works have investigated approaches to reduce model size, relatively little attention has been paid to techniques to improve batch throughput during inference
  • In many languages like Arabic, diacritics are used to specify pronunciations as well as meanings. Such diacritics are often omitted in written text, increasing the number of possible pronunciations and meanings for a word. This results in a more ambiguous text making computational processing on such text more difficult. Diacritic restoration is the task of restoring missing diacritics in the written text

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