Conventional seq2seq chatbot models attempt only to find sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the output sentences. In this paper, we investigate four models to scale or adjust the sentiment of the chatbot response: a persona-based model, reinforcement learning, a plug and play model, and CycleGAN, all based on the seq2seq model. We also develop machine-evaluated metrics to estimate whether the responses are reasonable given the input. These metrics, together with human evaluation, are used to analyze the performance of the four models in terms of different aspects; reinforcement learning and CycleGAN are shown to be very attractive.
National Technical University of Athens（雅典国家技术大学）；ATHENA R.C.（雅典娜研究中心）；Behavioral Signals
In this paper, we introduce EmpBot: an end-to-end empathetic chatbot. Empathetic conversational agents should not only understand what is being discussed, but also acknowledge the implied feelings of the conversation partner and respond appropriately. To this end, we propose a method based on a transformer pretrained language model (T5). Specifically, during finetuning we propose to use three objectives: response language modeling, sentiment understanding, and empathy forcing. The first objective is crucial for generating relevant and coherent responses, while the next ones are significant for…
Artificial Intelligence Application Research Center, Huawei Technologies（华为技术有限公司人工智能应用研究中心）
In this paper, we describe a data enhancement method for developing Emily, an emotion-affective open-domain chatbot. The proposed method is based on explicitly modeling positively transitioned (PT) sentiment data from multi-turn dialogues. We construct a dialogue corpus with PT sentiment data and will release it for public use. By fine-tuning a pretrained dialogue model using the produced PT-enhanced dialogues, we are able to develop an emotion-affective open-domain chatbot exhibiting close-to-human performance in various emotion-affective metrics. We evaluate Emily against a few state-of-the-…
Conventional seq2seq chatbot models only try to find the sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the output sentences. Some research works trying to modify the sentiment of the output sequences were reported. In this paper, we propose five models to scale or adjust the sentiment of the chatbot response: persona-based model, reinforcement learning, plug and play model, sentiment transformation network and cycleGAN, all based on the conventional seq2seq model. We also develop two evaluation metrics to estimate if the respo…
Technical University of Munich（慕尼黑工业大学）；BMW Group（宝马集团）
Many unanswerable adversarial questions fool the question-answer (QA) system with some plausible answers. Building a robust, frequently asked questions (FAQ) chatbot needs a large amount of diverse adversarial examples. Recent question generation methods are ineffective at generating many high-quality and diverse adversarial question-answer pairs from unstructured text. We propose the diversity controllable semantically valid adversarial attacker (DCSA), a high-quality, diverse, controllable method to generate standard and adversarial samples with a semantic graph. The fluent and semantically …
Meituan Group（美团集团）；National Engineering Research Center for Software Engineering, Peking University（北京大学软件工程国家工程研究中心）
Though widely used in industry, traditional task-oriented dialogue systems suffer from three bottlenecks: (i) difficult ontology construction (e.g., intents and slots); (ii) poor controllability and interpretability; (iii) annotation-hungry. In this paper, we propose to represent utterance with a simpler concept named Dialogue Action, upon which we construct a tree-structured TaskFlow and further build task-oriented chatbot with TaskFlow as core component. A framework is presented to automatically construct TaskFlow from large-scale dialogues and deploy online. Our experiments on real-world af…
School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；Pattern Recognition Center, WeChat AI, Tencent Inc（腾讯公司微信人工智能模式识别中心）
Generating a vivid, novel, and diverse essay with only several given topic words is a challenging task of natural language generation. In previous work, there are two problems left unsolved: neglect of sentiment beneath the text and insufficient utilization of topic-related knowledge. Therefore, we propose a novel Sentiment-Controllable topic-to-essay generator with a Topic Knowledge Graph enhanced decoder, named SCTKG, which is based on the conditional variational autoencoder (CVAE) framework. We firstly inject the sentiment information into the generator for controlling sentiment for each se…
Increasing happiness through conversations with artificial intelligence
Yale University（耶鲁大学）；Sainsbury Wellcome Centre for Neural Circuits and Behaviour, UCL（伦敦大学学院Sainsbury Wellcome神经环路与行为中心）；Google DeepMind（谷歌DeepMind）；University of Oxford（牛津大学）；Max Planck UCL Centre for Computational Psychiatry and Ageing Research（马克斯·普朗克伦敦大学学院计算精神病学与衰老研究中心）
Toyota Technological Institute at Chicago（芝加哥丰田技术研究院）；University of Chicago（芝加哥大学）；Tencent AI Lab（腾讯人工智能实验室）
We propose a simple and effective modeling framework for controlled generation of multiple, diverse outputs. We focus on the setting of generating the next sentence of a story given its context. As controllable dimensions, we consider several sentence attributes, including sentiment, length, predicates, frames, and automatically-induced clusters. Our empirical results demonstrate: (1) our framework is accurate in terms of generating outputs that match the target control values; (2) our model yields increased maximum metric scores compared to standard n-best list generation via beam search; (3)…
University of Virginia（弗吉尼亚大学）；Google Research（谷歌研究院）
NLP models are shown to suffer from robustness issues, i.e., a model's prediction can be easily changed under small perturbations to the input. In this work, we present a Controlled Adversarial Text Generation (CAT-Gen) model that, given an input text, generates adversarial texts through controllable attributes that are known to be invariant to task labels. For example, in order to attack a model for sentiment classification over product reviews, we can use the product categories as the controllable attribute which would not change the sentiment of the reviews. Experiments on real-world NLP da…
BTC-SAM: Leveraging LLMs for Generation of Bias Test Cases for Sentiment Analysis Models
Insight SFI Research Center on Data Analytics, Dublin City University（数据分析洞察SFI研究中心，都柏林城市大学）；Laboratoire LITAN, École supérieure en Sciences et Technologies de l’Informatique et du Numérique（LITAN实验室，信息与数字技术高等学院）；School of Computing, Dublin City University（计算学院，都柏林城市大学）；Plus Que Pro, Strasbourg, France（斯特拉斯堡法国Plus Que Pro公司）；ServiceNow, Zurich, Switzerland（瑞士苏黎世ServiceNow公司）；Department of Information Engineering and Computer Science, University of Trento（信息工程与计算机科学系，特伦托大学）；Télécom SudParis, SAMOVAR, Institut Polytechnique de Paris（巴黎理工学院SAMOVAR，Telecom SudParis）
Google Research（谷歌研究院）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
Style transfer is the task of rewriting a sentence into a target style while approximately preserving content. While most prior literature assumes access to a large style-labelled corpus, recent work (Riley et al. 2021) has attempted "few-shot" style transfer using only 3-10 sentences at inference for style extraction. In this work we study a relevant low-resource setting: style transfer for languages where no style-labelled corpora are available. We notice that existing few-shot methods perform this task poorly, often copying inputs verbatim. We push the state-of-the-art for few-shot style tr…
School of Computer and Communication Sciences（计算机与通信科学学院）；École polytechnique fédérale de Lausanne（洛桑联邦理工学院）
Open-domain conversational agents or chatbots are becoming increasingly popular in the natural language processing community. One of the challenges is enabling them to converse in an empathetic manner. Current neural response generation methods rely solely on end-to-end learning from large scale conversation data to generate dialogues. This approach can produce socially unacceptable responses due to the lack of large-scale quality data used to train the neural models. However, recent work has shown the promise of combining dialogue act/intent modelling and neural response generation. This hybr…
