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Hierarchical Recurrent Attention Network for Response Generation

AAAI Conferences

We study multi-turn response generation in chatbots where a response is generated according to a conversation context. Existing work has modeled the hierarchy of the context, but does not pay enough attention to the fact that words and utterances in the context are differentially important. As a result, they may lose important information in context and generate irrelevant responses. We propose a hierarchical recurrent attention network (HRAN) to model both the hierarchy and the importance variance in a unified framework. In HRAN, a hierarchical attention mechanism attends to important parts within and among utterances with word level attention and utterance level attention respectively. Empirical studies on both automatic evaluation and human judgment show that HRAN can significantly outperform state-of-the-art models for context based response generation.


TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts

arXiv.org Machine Learning

Scientific documents rely on both mathematics and text to communicate ideas. Inspired by the topical correspondence between mathematical equations and word contexts observed in scientific texts, we propose a novel topic model that jointly generates mathematical equations and their surrounding text (TopicEq). Using an extension of the correlated topic model, the context is generated from a mixture of latent topics, and the equation is generated by an RNN that depends on the latent topic activations. To experiment with this model, we create a corpus of 400K equation-context pairs extracted from a range of scientific articles from arXiv, and fit the model using a variational autoencoder approach. Experimental results show that this joint model significantly outperforms existing topic models and equation models for scientific texts. Moreover, we qualitatively show that the model effectively captures the relationship between topics and mathematics, enabling novel applications such as topic-aware equation generation, equation topic inference, and topic-aware alignment of mathematical symbols and words.


Topical Language Generation using Transformers

arXiv.org Artificial Intelligence

Large-scale transformer-based language models (LMs) demonstrate impressive capabilities in open text generation. However, controlling the generated text's properties such as the topic, style, and sentiment is challenging and often requires significant changes to the model architecture or retraining and fine-tuning the model on new supervised data. This paper presents a novel approach for Topical Language Generation (TLG) by combining a pre-trained LM with topic modeling information. We cast the problem using Bayesian probability formulation with topic probabilities as a prior, LM probabilities as the likelihood, and topical language generation probability as the posterior. In learning the model, we derive the topic probability distribution from the user-provided document's natural structure. Furthermore, we extend our model by introducing new parameters and functions to influence the quantity of the topical features presented in the generated text. This feature would allow us to easily control the topical properties of the generated text. Our experimental results demonstrate that our model outperforms the state-of-the-art results on coherency, diversity, and fluency while being faster in decoding.


Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication

arXiv.org Artificial Intelligence

Keep it Consistent: T opic-A ware Storytelling from an Image Stream via Iterative Multi-agent Communication Ruize Wang 1, Zhongyu Wei 2, Piji Li 3, Haijun Shan 4, Ji Zhang 4, Qi Zhang 5, Xuanjing Huang 5 1 Academy for Engineering and Technology, Fudan University, China 2 School of Data Science, Fudan University, China 3 Tencent AI Lab, China 4 Zhejiang Lab, China 5 School of Computer Science, Fudan University, China { rzwang18,zywei,qz,xjhuang} @fudan.edu.cn; Abstract Visual storytelling aims to generate a narrative paragraph from a sequence of images automatically. Existing approaches construct text description independently for each image and roughly concatenate them as a story, which leads to the problem of generating semantically incoherent content. In this paper, we proposed a new way for visual storytelling by introducing a topic description task to detect the global semantic context of an image stream. A story is then constructed with the guidance of the topic description. In order to combine the two generation tasks, we propose a multi-agent communication framework that regards the topic description generator and the story generator as two agents and learn them simultaneously via iterative updating mechanism. We validate our approach on VIST, where quantitative results, ablations, and human evaluation demonstrate our method's good ability in generating stories with higher quality compared to state-of-the-art methods. 1 Introduction Image-to-text generation is an important topic in artificial intelligence (AI) which connects computer vision (CV) and natural language processing (NLP). Popular tasks include image captioning (Karpathy and Fei-Fei 2015; Ren et al. 2017; Vinyals et al. 2017) and question answering (Antol et al. 2015; Y u et al. 2017; Fan et al. 2018a; Fan et al. 2018b), aiming at generating a short sentence or a phrase conditioned on certain visual information. It requires the model to understand the main idea of the image stream and generate coherent sentences. Most of existing methods (Huang et al. 2016; Liu et al. 2017; Y u, Bansal, and Berg 2017; Wang et al. 2018a) for visual storytelling extend approaches of image captioning without considering topic information of the image sequence, which causes the problem of generating semantically incoherent content.


An Emotion-controlled Dialog Response Generation Model with Dynamic Vocabulary

arXiv.org Artificial Intelligence

In response generation task, proper sentimental expressions can obviously improve the human-like level of the responses. However, for real application in online systems, high QPS (queries per second, an indicator of the flow capacity of on-line systems) is required, and a dynamic vocabulary mechanism has been proved available in improving speed of generative models. In this paper, we proposed an emotion-controlled dialog response generation model based on the dynamic vocabulary mechanism, and the experimental results show the benefit of this model.