37 research outputs found
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Inducing Symbolic Characteristics in Neural Question-Answering Systems via Data Interventions
Question Answering (QA) is a great way to test the natural language understanding of an artificial intelligence system. The recent advances in model architectures and large-scale datasets have led to the development of neural QA systems that surpass human performance on question answering. The reason behind the success of neural systems lies in their ability to directly learn features to extract answers from data. In contrast, symbolic systems encounter notable difficulties in scaling due to their restricted applicability to semi-structured or symbol-grounded data. Despite their reliance on structured data, symbolic systems demonstrate proficiency in executing deterministic operations and performing reasoning tasks. Conversely, neural systems exhibit limitations in reasoning, as they are (1) inconsistent, (2) unable to compose simple facts and perform complex reasoning, and (3) sensitive to changes in domain distribution.
In this dissertation, we present a range of data intervention schemes that facilitate in building consistent, decomposable, and generalizable neural QA systems. First, we show that purely neural systems are inconsistent and biased because most training and data collection procedures for neural systems make the independence assumptions and we explore two ways to address this problem. Second, we introduce a compositional QA dataset and show that neural QA methods lack decomposability. We propose a method to break down the complex questions using generated data into simpler, more manageable sub-questions to improve few-shot performance. Finally, we dissect the complex interactions among questions, answers, and documents learned by a neural QA system to assess their effectiveness towards generalization under a range of different data distributions through a series of generated data interventions and dynamic task sampling. Overall, we demonstrate how data interventions can be utilized to induce characteristics of symbolic systems into neural QA systems
Question Answering over Curated and Open Web Sources
The last few years have seen an explosion of research on the topic of
automated question answering (QA), spanning the communities of information
retrieval, natural language processing, and artificial intelligence. This
tutorial would cover the highlights of this really active period of growth for
QA to give the audience a grasp over the families of algorithms that are
currently being used. We partition research contributions by the underlying
source from where answers are retrieved: curated knowledge graphs, unstructured
text, or hybrid corpora. We choose this dimension of partitioning as it is the
most discriminative when it comes to algorithm design. Other key dimensions are
covered within each sub-topic: like the complexity of questions addressed, and
degrees of explainability and interactivity introduced in the systems. We would
conclude the tutorial with the most promising emerging trends in the expanse of
QA, that would help new entrants into this field make the best decisions to
take the community forward. Much has changed in the community since the last
tutorial on QA in SIGIR 2016, and we believe that this timely overview will
indeed benefit a large number of conference participants.Comment: SIGIR 2020 Tutoria