Search Results for author: Guan-Lin Chao

Found 5 papers, 1 papers with code

DeepCopy: Grounded Response Generation with Hierarchical Pointer Networks

no code implementations WS 2019 Semih Yavuz, Abhinav Rastogi, Guan-Lin Chao, Dilek Hakkani-Tur

Recent advances in neural sequence-to-sequence models have led to promising results for several language generation-based tasks, including dialogue response generation, summarization, and machine translation.

Machine Translation Response Generation +2

Learning Question-Guided Video Representation for Multi-Turn Video Question Answering

no code implementations WS 2019 Guan-Lin Chao, Abhinav Rastogi, Semih Yavuz, Dilek Hakkani-Tür, Jindong Chen, Ian Lane

Understanding and conversing about dynamic scenes is one of the key capabilities of AI agents that navigate the environment and convey useful information to humans.

Navigate Question Answering +2

BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer

1 code implementation5 Jul 2019 Guan-Lin Chao, Ian Lane

We focus on a specific condition, where the ontology is unknown to the state tracker, but the target slot value (except for none and dontcare), possibly unseen during training, can be found as word segment in the dialogue context.

Dialogue State Tracking

Speaker-Targeted Audio-Visual Models for Speech Recognition in Cocktail-Party Environments

no code implementations13 Jun 2019 Guan-Lin Chao, William Chan, Ian Lane

Speech recognition in cocktail-party environments remains a significant challenge for state-of-the-art speech recognition systems, as it is extremely difficult to extract an acoustic signal of an individual speaker from a background of overlapping speech with similar frequency and temporal characteristics.

speech-recognition Speech Recognition

City-Identification of Flickr Videos Using Semantic Acoustic Features

no code implementations12 Jul 2016 Benjamin Elizalde, Guan-Lin Chao, Ming Zeng, Ian Lane

In particular, we present a method to compute and use semantic acoustic features to perform city-identification and the features show semantic evidence of the identification.

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