Opinion entity extraction is a fundamental task in fine-grained opinion mining. Related studies generally extract aspects and/or opinion expressions without recognizing the relations between them. However, the relations are crucial for downstream tasks, including sentiment classification, opinion summarization, etc. In this paper, we explore Aspect-Opinion Pair Extraction (AOPE) task, which aims at extracting aspects and opinion expressions in pairs. To deal with this task, we propose Synchronous Double-channel Recurrent Network (SDRN) mainly consisting of an opinion entity extraction unit, a relation detection unit, and a synchronization unit. The opinion entity extraction unit and the relation detection unit are developed as two channels to extract opinion entities and relations simultaneously. Furthermore, within the synchronization unit, we design Entity Synchronization Mechanism (ESM) and Relation Synchronization Mechanism (RSM) to enhance the mutual benefit on the above two channels. To verify the performance of SDRN, we manually build three datasets based on SemEval 2014 and 2015 benchmarks. Extensive experiments demonstrate that SDRN achieves state-of-the-art performances.

We present MT-DNN, an open-source natural language understanding (NLU) toolkit that makes it easy for researchers and developers to train customized deep learning models. Built upon PyTorch and Transformers, MT-DNN is designed to facilitate rapid customization for a broad spectrum of NLU tasks, using a variety of objectives (classification, regression, structured prediction) and text encoders (e.g., RNNs, BERT, RoBERTa, UniLM). A unique feature of MT-DNN is its built-in support for robust and transferable learning using the adversarial multi-task learning paradigm. To enable efficient production deployment, MT-DNN supports multi-task knowledge distillation, which can substantially compress a deep neural model without significant performance drop. We demonstrate the effectiveness of MT-DNN on a wide range of NLU applications across general and biomedical domains. The software and pre-trained models will be publicly available at https://github.com/namisan/mt-dnn.