EOSwill target prediction source Softmax Layer LSTM Decoder Affective Attention LSTM Encoder Affective Embedding Affective Objective Function VAD Embedding Word Embedding VAD Embedding Affective Embedding Term Frequency

Affect conveys important implicit information in human communication. Having the capability to correctly express affect during human-machine conversations is one of the major milestones in artificial intelligence. In recent years, extensive research on open-domain neural conversational models has been conducted. However, embedding affect into such models is still under explored. In this paper, we propose an endto-end affect-rich open-domain neural conversational model that produces responses not only appropriate in syntax and semantics, but also with rich affect. Our model extends the Seq2Seq model and adopts VAD (Valence, Arousal and Dominance) affective notations to embed each word with affects. In addition, our model considers the effect of negators and intensifiers via a novel affective attention mechanism, which biases attention towards affect-rich words in input sentences. Lastly, we train our model with an affect-incorporated objective function to encourage the generation of affect-rich words in the output responses. Evaluations based on both perplexity and human evaluations show that our model outperforms the state-of-the-art baseline model of comparable size in producing natural and affect-rich responses. Introduction Affect is a psychological experience of feeling or emotion. As a vital part of human intelligence, having the capability to recognize, understand and express affect and emotions like human has been arguably one of the major milestones in artificial intelligence (Picard 1997). Open-domain conversational models aim to generate coherent and meaningful responses when given user input sentences. In recent years, neural network based generative conversational models relying on Sequence-to-Sequence network (Seq2Seq) (Sutskever, Vinyals, and Le 2014) have been widely adopted due to its success in neural machine translation. Seq2Seq based conversational models have the advantages of end-to-end training paradigm and unrestricted response space over conventional retrieval-based models. To make neural conversational models more engaging, various techniques have been proposed, such as using stochastic latent variable (Serban et al. 2017) to promote response diversity and encoding topic (Xing et al. 2017) into conversational models to produce more coherent responses. Copyright c © 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. However, embedding affect into neural conversational models has been seldom explored, despite that it has many benefits such as improving user satisfaction (Callejas, Griol, and López-Cózar 2011), fewer breakdowns (Martinovski and Traum 2003), and more engaged conversations (Robison, McQuiggan, and Lester 2009). For real-world applications, Fitzpatrick, Darcy, and Vierhile (2017) developed a rule-based empathic chatbot to deliver cognitive behavior therapy to young adults with depression and anxiety, and obtained significant results on depression reduction. Despite of these benefits, there are a few challenges in the affect embedding in neural conversational models that existing approaches fail to address: (i) It is difficult to capture the emotion of a sentence, partly because negators and intensifiers often change its polarity and strength. Handling negators and intensifiers properly still remains as a challenge in sentiment analysis. (ii) It is difficult to embed emotions naturally in responses with correct grammar and semantics (Ghosh et al. 2017). In this paper, we propose an end-to-end single-turn opendomain neural conversational model to address the aforementioned challenges to produce responses that are natural and affect-rich. Our model extends Seq2Seq model with attention (Luong, Pham, and Manning 2015). We leverage an external corpus (Warriner, Kuperman, and Brysbaert 2013) to provide affect knowledge for each word in the Valence, Arousal and Dominance (VAD) dimensions (Mehrabian 1996). We then incorporate the affect knowledge into the embedding layer of our model. VAD notation has been widely used as a dimensional representation of human emotions in psychology and various computational models, e.g., (Wang, Tan, and Miao 2016; Tang et al. 2017). 2D plots of selected words with extreme VAD values are shown in Figure 1. To capture the effect of negators and intensifiers, we propose a novel biased attention mechanism that explicitly considers negators and intensifiers in attention computation. To maintain correct grammar and semantics, we train our Seq2Seq model with a weighted cross-entropy loss that encourages the generation of affect-rich words without degrading language fluency. Our main contributions are summarized as follows: • For the first time, we propose a novel affective attention mechanism to incorporate the effect of negators and intensifiers in conversation modeling. Our mechanism inar X iv :1 81 1. 07 07 8v 1 [ cs . C L ] 1 7 N ov 2 01 8