Open-Retrieval Conversational Question Answering (2005.11364v1)

Abstract: 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.

Open-Retrieval Conversational Question Answering: Insights and Contributions

Recent advancements in intelligent assistant systems have underscored the pressing need for conversational search platforms that go beyond simple query-answer paradigms. The paper "Open-Retrieval Conversational Question Answering" by researchers from the University of Massachusetts Amherst and affiliated institutions introduces a novel framework, ORConvQA, that aims to integrate retrieval processes into conversational question answering (ConvQA) systems, hitherto simplified by using fixed candidate sets or pre-selected passages. This integration marks a significant step towards creating functional conversational search systems that can cater to more complex and realistic information-seeking interactions.

Introduction of the ORConvQA Framework

The ORConvQA framework is predicated on the understanding that conversational interfaces must be equipped to handle open-retrieval tasks where evidence needed to answer questions must be dynamically drawn from vast collections of documents. To support research in this direction, the authors present the OR-QuAC dataset, which merges conversation data and the expansive Wikipedia corpus. This dataset is structured to test systems' ability to retrieve relevant passages from a large database before answer extraction.

System Architecture

The paper outlines a comprehensive system architecture comprising three key components: a retriever, a reranker, and a reader, all employing Transformer-based models. The retriever utilizes a dual-encoder approach reminiscent of the ORQA method to ensure efficient evidence gathering via learnable dense representations of questions and passages. The retriever is pretrained on aggregated conversation history aligning with the query context, enabling it to offer significant advantages over traditional TF-IDF or BM25-based models.

The reranker component enhances the model's depth by refining the retrieval process and providing regularization benefits during training. This additional processing allows for improved passage rankings compared to conventional methods, as reflected by superior MRR and recall metrics in experiments. Finally, the reader component addresses the nuanced challenge of extracting answer spans from retrieved content, employing shared-normalization and optimizing processes for maximum contextual understanding.

Experimental Results and Implications

The experiments conducted on the OR-QuAC dataset demonstrate significant gains in accuracy metrics like F1 and HEQ when history modeling is implemented across system components. The inclusion of conversation history proves essential, with strategically selected history windows manifesting noticeable improvements in retrieval and answer prediction results.

Importantly, the paper's emphasis on learnable retrievers reflects a broader need in data-driven research to innovate beyond established methods. Learnable systems provide flexibility and are better suited to handle complex, context-laden interactions typical of genuine information-seeking scenarios. Despite the synthetic nature of the OR-QuAC data—where all dialog questions are linked to specific text sections—the methodology offers critical insights into structuring real-world ConvQA systems.

Future Directions

The authors identify avenues for future exploration, particularly in devising weak supervision and tunable retrieval systems to enhance adaptability in open-retrieval environments. The implications of this research are vast, potentially informing the development of AI-driven platforms equipped to handle diverse conversational contexts, thereby paving the way for more interactive and satisfying user experiences.

Conclusion

This paper contributes substantially to the field of conversational search by foregrounding the importance of retrieval in crafting effective QA systems. Through the proposed ORConvQA framework and corresponding empirical evaluations, the authors challenge prevailing limitations in ConvQA setups, advocating for more holistic and scalable solutions. These efforts represent a significant expansion of the toolkit available to researchers and developers as they strive to improve and refine intelligent assistant technologies.