2015 NaturalLanguageTranslationatthe

Subject Headings: Automated Machine Translation; Semi-Automated Tasks.

Notes

The fields of artificial intelligence (AI) and human-computer interaction (HCI) are influencing each other like never before. Widely used systems such as Google Translate, Facebook Graph Search, and RelateIQ hide the complexity of large-scale AI systems behind intuitive interfaces. But relations were not always so auspicious. The two fields emerged at different points in the history of computer science, with different influences, ambitions, and attendant biases. AI aimed to construct a rival, and perhaps a successor, to the human intellect. Early AI researchers such as McCarthy, Minsky, and Shannon were mathematicians by training, so theorem-proving and formal models were attractive research directions. In contrast, HCI focused more on empirical approaches to usability and human factors, both of which generally aim to make machines more useful to humans. Many attendees at the first CHI conference in 1983 were psychologists and engineers. Presented papers had titles such as “Design Principles for Human-Computer Interfaces” and “Psychological Issues in the Use of Icons in Command Menus," hardly appealing fare for mainstream AI researchers.

Since the 1960s, HCI has often been ascendant when setbacks in AI occurred, with successes and failures in the two fields redirecting mindshare and research funding.14 Although early figures such as Allen Newell and Herbert Simon made fundamental contributions to both fields, the competition and relative lack of dialogue between AI and HCI are curious. Both fields are broadly concerned with the connection between machines and intelligent human agents. What has changed recently is the deployment and adoption of user-facing AI systems. These systems need interfaces, leading to natural meeting points between the two fields.

Nowhere is this intersection more apropos than in natural language processing (NLP). Language translation is a concrete example. In practice, professional translators use suggestions from machine aids to construct final, high-quality translations. Increasingly, human translators are incorporating the output of machine translation (MT) systems such as Google Translate into their work. But how do we go beyond simple correction of machine mistakes? Recently, research groups at Stanford, Carnegie Mellon, and the European CasmaCat consortium have been investigating a human-machine model like that shown in Figure 1.

What is not widely known is this type of system was first envisioned in the early 1950s and developments in translation research figured significantly in the early dialogue between AI and HCI. The failed dreams of early MT researchers are not merely historical curiosities, but illustrations of how intellectual biases can marginalize pragmatic solutions, in this case a human-machine partnership for translation. As practicing AI and HCI researchers, we have found the conversation today has many of the same features, so the historical narrative can be instructive. In this article, we first recount that history. Then we summarize the recent breakthroughs in translation made possible by a healthy AI-HCI collaboration.

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We have shown a human-machine system design for language translation benefits both human users — who produce higher-quality translations — and machine agents, which can refine their models given rich feedback. Mixed-initiative MT systems were conceived as early as 1951, but the idea was marginalized due to biases in the AI research community. The new results were obtained by combining insights from AI and HCI, two communities with similar strategic aims but surprisingly limited interaction for many decades. Other problems in NLP such as question answering and speech transcription could benefit from interactive systems not unlike the one we have proposed for translation. Significant issues to consider in the design of these systems are: Where to insert the human efficiently in the processing loop. How to maximize human utility even when machine suggestions are sometimes fatally flawed. How to isolate and then improve the contributions of specific interface interventions (for example, full-sentence suggestions vs. autocomplete phrases) in the task setting.

These questions were anticipated in the translation community long before AI and HCI were organized fields. New dialogue between the fields is yielding fresh approaches that apply not only to translation, but to other systems that attempt to augment and learn from the human intellect.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 NaturalLanguageTranslationatthe	Christopher D. Manning Spence Green Jeffrey Heer			Natural Language Translation at the Intersection of AI and HCI				10.1145/2767151		2015