Reading Wikipedia to Answer Open-Domain Questions

This paper proposes to tackle open- domain question answering using Wikipedia as the unique knowledge source: the answer to any factoid question is a text span in a Wikipedia article. This task of machine reading at scale combines the challenges of document retrieval (finding the relevant articles) with that of machine comprehension of text (identifying the answer spans from those articles). Our approach combines a search component based on bigram hashing and TF-IDF matching with a multi-layer recurrent neural network model trained to detect answers in Wikipedia paragraphs. Our experiments on multiple existing QA datasets indicate that (1) both modules are highly competitive with respect to existing counterparts and (2) multitask learning using distant supervision on their combination is an effective complete system on this challenging task.Comment: ACL2017, 10 page

Open-Retrieval Conversational Question Answering

Conversational search is one of the ultimate goals of information retrieval. Recent research approaches conversational search by simplified settings of response ranking and conversational question answering, where an answer is either selected from a given candidate set or extracted from a given passage. These simplifications neglect the fundamental role of retrieval in conversational search. To address this limitation, we introduce an open-retrieval conversational question answering (ORConvQA) setting, where we learn to retrieve evidence from a large collection before extracting answers, as a further step towards building functional conversational search systems. We create a dataset, OR-QuAC, to facilitate research on ORConvQA. We build an end-to-end system for ORConvQA, featuring a retriever, a reranker, and a reader that are all based on Transformers. Our extensive experiments on OR-QuAC demonstrate that a learnable retriever is crucial for ORConvQA. We further show that our system can make a substantial improvement when we enable history modeling in all system components. Moreover, we show that the reranker component contributes to the model performance by providing a regularization effect. Finally, further in-depth analyses are performed to provide new insights into ORConvQA.Comment: Accepted to SIGIR'2

Towards Zero-Shot Frame Semantic Parsing for Domain Scaling

State-of-the-art slot filling models for goal-oriented human/machine conversational language understanding systems rely on deep learning methods. While multi-task training of such models alleviates the need for large in-domain annotated datasets, bootstrapping a semantic parsing model for a new domain using only the semantic frame, such as the back-end API or knowledge graph schema, is still one of the holy grail tasks of language understanding for dialogue systems. This paper proposes a deep learning based approach that can utilize only the slot description in context without the need for any labeled or unlabeled in-domain examples, to quickly bootstrap a new domain. The main idea of this paper is to leverage the encoding of the slot names and descriptions within a multi-task deep learned slot filling model, to implicitly align slots across domains. The proposed approach is promising for solving the domain scaling problem and eliminating the need for any manually annotated data or explicit schema alignment. Furthermore, our experiments on multiple domains show that this approach results in significantly better slot-filling performance when compared to using only in-domain data, especially in the low data regime.Comment: 4 pages + 1 reference

Preclinical risk of bias assessment and PICO extraction using natural language processing

Drug development starts with preclinical studies which test the efficacy and toxicology of potential candidates in living animals, before proceeding to clinical trials examined on human subjects. Many drugs shown to be effective in preclinical animal studies fail in clinical trials, indicating the potential reproducibility issues and translation failure. To obtain less biased research findings, systematic reviews are performed to collate all relevant evidence from publications. However, systematic reviews are time-consuming and researchers have advocated the use of automation techniques to speed the process and reduce human efforts. Good progress has been made in implementing automation tools into reviews for clinical trials while the tools developed for preclinical systematic reviews are scarce. Tools for preclinical systematic reviews should be designed specifically because preclinical experiments differ from clinical trials. In this thesis, I explore natural language processing models for facilitating two stages in preclinical systematic reviews: risk of bias assessment and PICO extraction. There are a range of measures used to reduce bias in animal experiments and many checklist criteria require the reporting of those measures in publications. In the first part of the thesis, I implement several binary classification models to indicate the reporting of random allocation to groups, blinded assessment of outcome, conflict of interests, compliance of animal welfare regulations, and statement of animal exclusions in preclinical publications. I compare traditional machine learning classifiers with several text representation methods, convolutional/recurrent/hierarchical neural networks, and propose two strategies to adapt BERT models to long documents. My findings indicate that neural networks and BERT-based models achieve better performance than traditional classifiers and rule-based approaches. The attention mechanism and hierarchical architecture in neural networks do not improve performance but are useful for extracting relevant words or sentences from publications to inform users’ judgement. The advantages of the transformer structure are hindered when documents are long and computing resources are limited. In literature retrieval and citation screening of published evidence, the key elements of interest are Population, Intervention, Comparator and Outcome, which compose the framework of PICO. In the second part of the thesis, I first apply several question answering models based on attention flows and transformers to extract phrases describing intervention or method of induction of disease models from clinical abstracts and preclinical full texts. For preclinical datasets describing multiple interventions or induction methods in the full texts, I apply additional unsupervised information retrieval methods to extract relevant sentences. The question answering models achieve good performance when the text is at abstract-level and contains only one intervention or induction method, while for truncated documents with multiple PICO mentions, the performance is less satisfactory. Considering this limitation, I then collect preclinical abstracts with finer-grained PICO annotations and develop named entity recognition models for extraction of preclinical PICO elements including Species, Strain, Induction, Intervention, Comparator and Outcome. I decompose PICO extraction into two independent tasks: 1) PICO sentences classification, and 2) PICO elements detection. For PICO extraction, BERT-based models pre-trained from biomedical corpus outperform recurrent networks and the conditional probabilistic module only shows advantages in recurrent networks. Self-training strategy applied to enlarge training set from unlabelled abstracts yields better performance for PICO elements which lack enough amount of instances. Experimental results demonstrate the possibilities of facilitating preclinical risk of bias assessment and PICO extraction by natural language processing