Comprehensive Evaluation for a Large Scale Knowledge Graph Question Answering Service

Question answering systems for knowledge graph (KGQA), answer factoid questions based on the data in the knowledge graph. KGQA systems are complex because the system has to understand the relations and entities in the knowledge-seeking natural language queries and map them to structured queries against the KG to answer them. In this paper, we introduce Chronos, a comprehensive evaluation framework for KGQA at industry scale. It is designed to evaluate such a multi-component system comprehensively, focusing on (1) end-to-end and component-level metrics, (2) scalable to diverse datasets and (3) a scalable approach to measure the performance of the system prior to release. In this paper, we discuss the unique challenges associated with evaluating KGQA systems at industry scale, review the design of Chronos, and how it addresses these challenges. We will demonstrate how it provides a base for data-driven decisions and discuss the challenges of using it to measure and improve a real-world KGQA system.

Audiovisual Speech Synthesis using Tacotron2

Audiovisual speech synthesis is the problem of synthesizing a talking face while maximizing the coherency of the acoustic and visual speech. In this paper, we propose and compare two audiovisual speech synthesis systems for 3D face models. The first system is the AVTacotron2, which is an end-to-end text-to-audiovisual speech synthesizer based on the Tacotron2 architecture. AVTacotron2 converts a sequence of phonemes representing the sentence to synthesize into a sequence of acoustic features and the corresponding controllers of a face model. The output acoustic features are used to condition a WaveRNN to reconstruct the speech waveform, and the output facial controllers are used to generate the corresponding video of the talking face. The second audiovisual speech synthesis system is modular, where acoustic speech is synthesized from text using the traditional Tacotron2. The reconstructed acoustic speech signal is then used to drive the facial controls of the face model using an independently trained audio-to-facial-animation neural network. We further condition both the end-to-end and modular approaches on emotion embeddings that encode the required prosody to generate emotional audiovisual speech. We analyze the performance of the two systems and compare them to the ground truth videos using subjective evaluation tests. The end-to-end and modular systems are able to synthesize close to human-like audiovisual speech with mean opinion scores (MOS) of 4.1 and 3.9, respectively, compared to a MOS of 4.1 for the ground truth generated from professionally recorded videos. While the end-to-end system gives a better overall quality, the modular approach is more flexible and the quality of acoustic speech and visual speech synthesis is almost independent of each other.