Natural Language Understanding (NLU) is a branch of Natural Language Processing (NLP) that uses intelligent computer software to understand texts that encode human knowledge. Recent years have witnessed notable progress across various NLU tasks with deep learning techniques, especially with pretrained language models. Besides proposing more advanced model architectures, constructing more reliable and trustworthy datasets also plays a huge role in improving NLU systems, without which it would be impossible to train a decent NLU model. It's worth noting that the human ability of understanding natural language is flexible and robust. On the contrary, most of existing NLU systems fail to achieve desirable performance on out-of-domain data or struggle on handling challenging items (e.g., inherently ambiguous items, adversarial items) in the real world. Therefore, in order to have NLU models understand human language more effectively, it is expected to prioritize the study on robust natural language understanding.
In this thesis, we deem that NLU systems are consisting of two components: NLU models and NLU datasets. As such, we argue that, to achieve robust NLU, the model architecture/training and the dataset are equally important. Specifically, we will focus on three NLU tasks to illustrate the robustness problem in different NLU tasks and our contributions (i.e., novel models and new datasets) to help achieve more robust natural language understanding. The major technical contributions of this thesis are:
(1) We study how to utilize diversity boosters (e.g., beam search & QPP) to help neural question generator synthesize diverse QA pairs, upon which a Question Answering (QA) system is trained to improve the generalization on the unseen target domain. It's worth mentioning that our proposed QPP (question phrase prediction) module, which predicts a set of valid question phrases given an answer evidence, plays an important role in improving the cross-domain generalizability for QA systems. Besides, a target-domain test set is constructed and approved by the community to help evaluate the model robustness under the cross-domain generalization setting.
(2) We investigate inherently ambiguous items in Natural Language Inference, for which annotators don't agree on the label. Ambiguous items are overlooked in the literature but often occurring in the real world. We build an ensemble model, AAs (Artificial Annotators), that simulates underlying annotation distribution to effectively identify such inherently ambiguous items. Our AAs are better at handling inherently ambiguous items since the model design captures the essence of the problem better than vanilla model architectures.
(3) We follow a standard practice to build a robust dataset for FAQ retrieval task, COUGH. In our dataset analysis, we show how COUGH better reflects the challenge of FAQ retrieval in the real situation than its counterparts. The imposed challenge will push forward the boundary of research on FAQ retrieval in real scenarios.
Moving forward, the ultimate goal for robust natural language understanding is to build NLU models which can behave humanly. That is, it's expected that robust NLU systems are capable to transfer the knowledge from training corpus to unseen documents more reliably and survive when encountering challenging items even if the system doesn't know a priori of users' inputs.No embargoAcademic Major: Computer Science and EngineeringAcademic Major: Industrial and Systems Engineerin
