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

Guan-Lin Chao, Abhinav Rastogi, Semih Yavuz, Dilek Hakkani-Tür, Jindong Chen, Ian Lane
In SIGDIAL 2019 and SIGIR 2019 Workshop on Conversational Interaction Systems
[bib] [pdf] [slides] [poster]

title={Learning Question-Guided Video Representation for Multi-Turn Video Question Answering},
author={Chao, Guan-Lin and Rastogi, Abhinav and Yavuz, Semih and Hakkani-T{\"u}r, Dilek and Chen, Jindong and Lane, Ian},

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. Video question answering is a specific scenario of such AI-human interaction where an agent generates a natural language response to a question regarding the video of a dynamic scene. Incorporating features from multiple modalities, which often provide supplementary information, is one of the challenging aspects of video question answering. Furthermore, a question often concerns only a small segment of the video, hence encoding the entire video sequence using a recurrent neural network is not computationally efficient. Our proposed question-guided video representation module efficiently generates the token-level video summary guided by each word in the question. The learned representations are then fused with the question to generate the answer. Through empirical evaluation on the Audio Visual Scene-aware Dialog (AVSD) dataset, our proposed models in single-turn and multi-turn question answering achieve state-of-the-art performance on several automatic natural language generation evaluation metrics.Abstract

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

Guan-Lin Chao, Ian Lane
[bib] [pdf] [code] [slides]

title={{BERT-DST}: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer},
author={Chao, Guan-Lin and Lane, Ian},

An important yet rarely tackled problem in dialogue state tracking (DST) is scalability for dynamic ontology (e.g., movie, restaurant) and unseen slot values. 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. Prior approaches often rely on candidate generation from n-gram enumeration or slot tagger outputs, which can be inefficient or suffer from error propagation. We propose BERT-DST, an end-to-end dialogue state tracker which directly extracts slot values from the dialogue context. We use BERT as dialogue context encoder whose contextualized language representations are suitable for scalable DST to identify slot values from their semantic context. Furthermore, we employ encoder parameter sharing across all slots with two advantages: (1) Number of parameters does not grow linearly with the ontology. (2) Language representation knowledge can be transferred among slots. Empirical evaluation shows BERT-DST with cross-slot parameter sharing outperforms prior work on the benchmark scalable DST datasets Sim-M and Sim-R, and achieves competitive performance on the standard DSTC2 and WOZ 2.0 datasets.

Audio-Visual TED Corpus: Enhancing the TED-LIUM Corpus with Facial Information, Contextual Text and Object Recognition

Guan-Lin Chao, Chih Chi Hu, Bing Liu, John Paul Shen, Ian Lane
In UbiComp 2019 Workshop on Continual and Multimodal Learning for Internet of Things
[bib] [pdf]

title={Audio-Visual {TED} Corpus: Enhancing the {TED-LIUM} Corpus with Facial Information, Contextual Text and Object Recognition},
author={Chao, Guan-Lin and Hu, Chih Chi and Liu, Bing and Shen, John Paul and Lane, Ian},
booktitle={UbiComp Workshop on Continual and Multimodal Learning for Internet of Things},

We present a variety of new visual features in extension to the TED-LIUM corpus. We re-aligned the original TED talk audio transcriptions with official TED.com videos. By utilizing state-of-the-art models for face and facial landmarks detection, optical character recognition, object detection and classification, we extract four new visual features that can be used for Large-Vocabulary Continuous Speech Recognition (LVCSR) systems, including facial images, landmarks, text, and objects in the scenes. The facial images and landmarks can be used in combination with audio for audio-visual acoustic modeling where the visual modality provides robust features in adverse acoustic environments. The contextual information, i.e. extracted text and detected objects in the scene can be used as prior knowledge to create contextual language models. Experimental results showed the efficacy of using visual features on top of acoustic features for speech recognition in overlapping speech scenarios.

Deep Speaker Embedding for Speaker-Targeted Automatic Speech Recognition

Guan-Lin Chao, John Paul Shen, Ian Lane
In International Conference on Natural Language Processing and Information Retrieval (NLPIR) 2019 (Best Paper)
[bib] [pdf]

title={Deep Speaker Embedding for Speaker-Targeted Automatic Speech Recognition},
author={Chao, Guan-Lin and Shen, John Paul and Lane, Ian},
booktitle={International Conference on Natural Language Processing and Information Retrieval (NLPIR)},

In this work, we investigate three types of deep speaker embedding as text-independent features for speaker-targeted speech recognition in cocktail party environments. The text-independent speaker embedding is extracted from the target speaker’s existing speech segment (i-vector and x-vector) or face image (f-vector), which is concatenated with acoustic features of any new speech utterances as input features. Since the proposed model extracts the speaker embedding of the target speaker once and for all, it is computationally more efficient than many prior approaches which estimate the target speaker’s characteristics on the fly. Empirical evaluation shows that using speaker embedding along with acoustic features improves Word Error Rate over the audio-only model, from 65.7% to 29.5%. Among the three types of speaker embedding, x-vector and f-vector show robustness against environment variations while i-vector tends to overfit to the specific speaker and environment condition.