RESEARCH

NLP

Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue

November 5, 2019

Abstract

Traditional recommendation systems produce static rather than interactive recommendations invariant to a user’s specific requests, clarifications, or current mood, and can suffer from the cold-start problem if their tastes are unknown. These issues can be alleviated by treating recommendation as an interactive dialogue task instead, where an expert recommender can sequentially ask about someone’s preferences, react to their requests, and recommend more appropriate items. In this work, we collect a goal-driven recommendation dialogue dataset (GoRecDial), which consists of 9,125 dialogue games and 81,260 conversation turns between pairs of human workers recommending movies to each other. The task is specifically designed as a cooperative game between two players working towards a quantifiable common goal. We leverage the dataset to develop an end-to-end dialogue system that can simultaneously converse and recommend. Models are first trained to imitate the behavior of human players without considering the task goal itself (supervised training). We then fine-tune our models on simulated bot-bot conversations between two paired pre-trained models (bot-play), in order to achieve the dialogue goal. Our experiments show that models fine-tuned with bot-play learn improved dialogue strategies, reach the dialogue goal more often when paired with a human, and are rated as more consistent by humans compared to models trained without bot-play. The dataset and code are publicly available through the ParlAI framework.

Download the Paper

Related Publications

October 08, 2016

COMPUTER VISION

NLP

Learning Visual Features from Large Weakly Supervised Data | Facebook AI Research

Convolutional networks trained on large supervised datasets produce visual features which form the basis for the state-of-the-art in many computer-vision problems. Further improvements of these visual features will likely require even larger…

Armand Joulin, Laurens van der Maaten, Allan Jabri, Nicolas Vasilache

October 08, 2016

September 15, 2019

COMPUTER VISION

NLP

Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions | Facebook AI Research

We propose a fully convolutional sequence-to-sequence encoder architecture with a simple and efficient decoder. Our model improves WER on LibriSpeech while being an order of magnitude more efficient than a strong RNN baseline. Key to our…

Awni Hannun, Ann Lee, Qiantong Xu, Ronan Collobert

September 15, 2019

September 10, 2019

NLP

Bridging the Gap Between Relevance Matching and Semantic Matching for Short Text Similarity Modeling | Facebook AI Research

A core problem of information retrieval (IR) is relevance matching, which is to rank documents by relevance to a user’s query. On the other hand, many NLP problems, such as question answering and paraphrase identification, can be considered…

Jinfeng Rao, Linqing Liu, Yi Tay, Wei Yang, Peng Shi, Jimmy Lin

September 10, 2019

June 16, 2019

NLP

On the Idiosyncrasies of the Mandarin Chinese Classifier System | Facebook AI Research

While idiosyncrasies of the Chinese classifier system have been a richly studied topic among linguists (Adams and Conklin, 1973; Erbaugh, 1986; Lakoff, 1986), not much work has been done to quantify them with statistical methods. In this paper,…

Shijia Liu, Hongyuan Mei, Adina Williams, Ryan Cotterell

June 16, 2019

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.