The widespread use of machine translation (MT) has driven the need for effective automatic quality estimation (AQE) methods. How to enhance the interpretability of MT output quality estimation is well worth exploring in the industry. From the perspective of the alignment of named entities (NEs) in the source and translated sentences, we construct a multilingual knowledge graph (KG) consisting of domain-specific NEs, and design a KG-based interpretable quality estimation (QE) system for machine translations (KG-IQES). KG-IQES effectively estimates the translation quality without relying on reference translations. Its effectiveness has been verified in our business scenarios.

This paper describes our solution for Sere- TOD Challenge Track 1: Information extraction from dialog transcripts. We propose a token-pair framework to simultaneously identify entity and value mentions and link them into corresponding triples. As entity mentions are usually coreferent, we adopt a baseline model for coreference resolution. We exploit both annotated transcripts and unsupervised dialogs for training. With model ensemble and post-processing strategies, our system significantly outperforms the baseline solution and ranks first in triple f1 and third in entity f1.