Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5427–5436 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 5427 Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading Lianhui Qin†, Michel Galley‡, Chris Brockett‡, Xiaodong Liu‡, Xiang Gao‡, Bill Dolan‡, Yejin Choi† and Jianfeng Gao‡ † University of Washington, Seattle, WA, USA ‡ Microsoft Research, Redmond, WA, USA {lianhuiq,yejin}@cs.washington.edu {mgalley,Chris.Brockett,xiaodl,xiag,billdol,jfgao}@microsoft.com Abstract Although neural conversation models are ef- fective in learning how to produce fluent re- sponses, their primary challenge lies in know- ing what to say to make the conversation con- tentful and non-vacuous. We present a new end-to-end approach to contentful neural con- versation that jointly models response gener- ation and on-demand machine reading. The key idea is to provide the conversation model with relevant long-form text on the fly as a source of external knowledge. The model performs QA-style reading comprehension on this text in response to each conversational turn, thereby allowing for more focused inte- gration of external knowledge than has been possible in prior approaches. To support fur- ther research on knowledge-grounded conver- sation, we introduce a new large-scale conver- sation dataset grounded in external web pages (2.8M turns, 7.4M sentences of grounding). Both human evaluation and automated metrics show that our approach results in more con- tentful responses compared to a variety of pre- vious methods, improving both the informa- tiveness and diversity of generated output. 1 Introduction While end-to-end neural conversation models (Shang et al., 2015; Sordoni et al., 2015; Vinyals and Le, 2015; Serban et al., 2016; Li et al., 2016a; Gao et al., 2019a, etc.) are effective in learning how to be fluent, their responses are often vacu- ous and uninformative. A primary challenge thus lies in modeling what to say to make the conver- sation contentful. Several recent approaches have attempted to address this difficulty by condition- ing the language decoder on external information sources, such as knowledge bases (Agarwal et al., 2018; Liu et al., 2018a), review posts (Ghazvinine- jad et al., 2018; Moghe et al., 2018), and even im- ages (Das et al., 2017; Mostafazadeh et al., 2017). …… …… She holds the Guinness world record for surviving the highest fall without a parachute: 10,160 metres (33,330 ft). A woman fell 30,000 feet from an airplane and survived. Well if she only fell a few hundred meters and survived then I 'm not impressed at all. The page states that a 2009 report found the plane only fell several hundred meters. Still pretty incredible , but quite a bit different that 10,000 meters. In 2005, Vulović‘s fall was recreated by the American television MythBusters. Four years later, […] two Prague- based journalists, claimed that Flight 367 had been mistaken for an enemy aircraft and shot down by the Czechoslovak Air Force at an altitude of 800 metres (2,600 ft). Figure 1: Users discussing a topic defined by a Wikipedia article. In this real-world example from our Reddit dataset, information …

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