As a crucial component in task-oriented dialog systems, the Natural Language Generation (NLG) module converts a dialog act represented in a semantic form into a response in natural language. The success of traditional template-based or statistical models typically relies on heavily annotated data, which is infeasible for new domains. Therefore, it is pivotal for an NLG system to generalize well with limited labelled data in real applications. To this end, we present FewShotWoz, the first NLG benchmark to simulate the few-shot learning setting in task-oriented dialog systems. Further, we develop the SC-GPT model. It is pre-trained on a large set of annotated NLG corpus to acquire the controllable generation ability, and fine-tuned with only a few domain-specific labels to adapt to new domains. Experiments on FewShotWoz and the large Multi-Domain-WOZ datasets show that the proposed SC-GPT significantly outperforms existing methods, measured by various automatic metrics and human evaluations.

22
下载
关闭预览

相关内容

小样本学习(Few-Shot Learning,以下简称 FSL )用于解决当可用的数据量比较少时,如何提升神经网络的性能。在 FSL 中,经常用到的一类方法被称为 Meta-learning。和普通的神经网络的训练方法一样,Meta-learning 也包含训练过程和测试过程,但是它的训练过程被称作 Meta-training 和 Meta-testing。

简介:

作为面向任务的对话系统中的关键组件,自然语言生成(NLG)模块将以语义形式表示的对话行为转换为自然语言的响应。传统的基于模板或统计模型的成功通常依赖于带有大量注释的数据,这对于新领域而言是不可行的。因此,对于NLG系统而言,在实际应用中使用有限的标记数据很好地泛化至关重要。为此,我们展示了FewShotWOZ,这是第一个NLG基准测试,用于模拟面向任务的对话系统中的少量学习设置。此外,我们开发了SC-GPT模型。它在大量带注释的NLG语料库上进行了预训练,以获取可控的生成能力,并仅用少数几个特定于域的标签进行微调以适应新的域。在FewShotWOZ和大型Multi-Domain-WOZ数据集上进行的实验表明,通过各种自动指标和人工评估,提出的SC-GPT明显优于现有方法。

成为VIP会员查看完整内容
0
20

Neural network models usually suffer from the challenge of incorporating commonsense knowledge into the open-domain dialogue systems. In this paper, we propose a novel knowledge-aware dialogue generation model (called TransDG), which transfers question representation and knowledge matching abilities from knowledge base question answering (KBQA) task to facilitate the utterance understanding and factual knowledge selection for dialogue generation. In addition, we propose a response guiding attention and a multi-step decoding strategy to steer our model to focus on relevant features for response generation. Experiments on two benchmark datasets demonstrate that our model has robust superiority over compared methods in generating informative and fluent dialogues. Our code is available at https://github.com/siat-nlp/TransDG.

0
12
下载
预览

We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer). Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch transformer to attain a performance close to human both in terms of automatic and human evaluation in single-turn dialogue settings. We show that conversational systems that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline systems. The pre-trained model and training pipeline are publicly released to facilitate research into neural response generation and the development of more intelligent open-domain dialogue systems.

0
5
下载
预览

In this paper, we propose Latent Relation Language Models (LRLMs), a class of language models that parameterizes the joint distribution over the words in a document and the entities that occur therein via knowledge graph relations. This model has a number of attractive properties: it not only improves language modeling performance, but is also able to annotate the posterior probability of entity spans for a given text through relations. Experiments demonstrate empirical improvements over both a word-based baseline language model and a previous approach that incorporates knowledge graph information. Qualitative analysis further demonstrates the proposed model's ability to learn to predict appropriate relations in context.

0
17
下载
预览

We propose a novel conditioned text generation model. It draws inspiration from traditional template-based text generation techniques, where the source provides the content (i.e., what to say), and the template influences how to say it. Building on the successful encoder-decoder paradigm, it first encodes the content representation from the given input text; to produce the output, it retrieves exemplar text from the training data as "soft templates," which are then used to construct an exemplar-specific decoder. We evaluate the proposed model on abstractive text summarization and data-to-text generation. Empirical results show that this model achieves strong performance and outperforms comparable baselines.

0
4
下载
预览

Pre-trained language model representations have been successful in a wide range of language understanding tasks. In this paper, we examine different strategies to integrate pre-trained representations into sequence to sequence models and apply it to neural machine translation and abstractive summarization. We find that pre-trained representations are most effective when added to the encoder network which slows inference by only 14%. Our experiments in machine translation show gains of up to 5.3 BLEU in a simulated resource-poor setup. While returns diminish with more labeled data, we still observe improvements when millions of sentence-pairs are available. Finally, on abstractive summarization we achieve a new state of the art on the full text version of CNN/DailyMail.

0
3
下载
预览

This paper presents our latest investigations on dialog act (DA) classification on automatically generated transcriptions. We propose a novel approach that combines convolutional neural networks (CNNs) and conditional random fields (CRFs) for context modeling in DA classification. We explore the impact of transcriptions generated from different automatic speech recognition systems such as hybrid TDNN/HMM and End-to-End systems on the final performance. Experimental results on two benchmark datasets (MRDA and SwDA) show that the combination CNN and CRF improves consistently the accuracy. Furthermore, they show that although the word error rates are comparable, End-to-End ASR system seems to be more suitable for DA classification.

0
3
下载
预览

Few-shot Learning aims to learn classifiers for new classes with only a few training examples per class. Existing meta-learning or metric-learning based few-shot learning approaches are limited in handling diverse domains with various number of labels. The meta-learning approaches train a meta learner to predict weights of homogeneous-structured task-specific networks, requiring a uniform number of classes across tasks. The metric-learning approaches learn one task-invariant metric for all the tasks, and they fail if the tasks diverge. We propose to deal with these limitations with meta metric learning. Our meta metric learning approach consists of task-specific learners, that exploit metric learning to handle flexible labels, and a meta learner, that discovers good parameters and gradient decent to specify the metrics in task-specific learners. Thus the proposed model is able to handle unbalanced classes as well as to generate task-specific metrics. We test our approach in the `$k$-shot $N$-way' few-shot learning setting used in previous work and new realistic few-shot setting with diverse multi-domain tasks and flexible label numbers. Experiments show that our approach attains superior performances in both settings.

0
11
下载
预览

Automatic generation of paraphrases from a given sentence is an important yet challenging task in natural language processing (NLP), and plays a key role in a number of applications such as question answering, search, and dialogue. In this paper, we present a deep reinforcement learning approach to paraphrase generation. Specifically, we propose a new framework for the task, which consists of a \textit{generator} and an \textit{evaluator}, both of which are learned from data. The generator, built as a sequence-to-sequence learning model, can produce paraphrases given a sentence. The evaluator, constructed as a deep matching model, can judge whether two sentences are paraphrases of each other. The generator is first trained by deep learning and then further fine-tuned by reinforcement learning in which the reward is given by the evaluator. For the learning of the evaluator, we propose two methods based on supervised learning and inverse reinforcement learning respectively, depending on the type of available training data. Empirical study shows that the learned evaluator can guide the generator to produce more accurate paraphrases. Experimental results demonstrate the proposed models (the generators) outperform the state-of-the-art methods in paraphrase generation in both automatic evaluation and human evaluation.

0
3
下载
预览

End-to-end task-oriented dialog systems usually suffer from the challenge of incorporating knowledge bases. In this paper, we propose a novel yet simple end-to-end differentiable model called memory-to-sequence (Mem2Seq) to address this issue. Mem2Seq is the first neural generative model that combines the multi-hop attention over memories with the idea of pointer network. We empirically show how Mem2Seq controls each generation step, and how its multi-hop attention mechanism helps in learning correlations between memories. In addition, our model is quite general without complicated task-specific designs. As a result, we show that Mem2Seq can be trained faster and attain the state-of-the-art performance on three different task-oriented dialog datasets.

0
7
下载
预览
小贴士
相关论文
Jian Wang,Junhao Liu,Wei Bi,Xiaojiang Liu,Kejing He,Ruifeng Xu,Min Yang
12+阅读 · 2019年12月16日
DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
Yizhe Zhang,Siqi Sun,Michel Galley,Yen-Chun Chen,Chris Brockett,Xiang Gao,Jianfeng Gao,Jingjing Liu,Bill Dolan
5+阅读 · 2019年11月1日
Hiroaki Hayashi,Zecong Hu,Chenyan Xiong,Graham Neubig
17+阅读 · 2019年8月21日
Quan Kong,Bin Tong,Martin Klinkigt,Yuki Watanabe,Naoto Akira,Tomokazu Murakami
3+阅读 · 2019年6月17日
Hao Peng,Ankur P. Parikh,Manaal Faruqui,Bhuwan Dhingra,Dipanjan Das
4+阅读 · 2019年4月9日
Sergey Edunov,Alexei Baevski,Michael Auli
3+阅读 · 2019年4月1日
Context-aware Neural-based Dialog Act Classification on Automatically Generated Transcriptions
Daniel Ortega,Chia-Yu Li,Gisela Vallejo,Pavel Denisov,Ngoc Thang Vu
3+阅读 · 2019年2月28日
Yu Cheng,Mo Yu,Xiaoxiao Guo,Bowen Zhou
11+阅读 · 2019年1月26日
Paraphrase Generation with Deep Reinforcement Learning
Zichao Li,Xin Jiang,Lifeng Shang,Hang Li
3+阅读 · 2018年8月23日
Andrea Madotto,Chien-Sheng Wu,Pascale Fung
7+阅读 · 2018年5月21日
相关VIP内容
专知会员服务
21+阅读 · 2020年4月7日
最新BERT相关论文清单,BERT-related Papers
专知会员服务
32+阅读 · 2019年9月29日
相关资讯
【ACL2020放榜!】事件抽取、关系抽取、NER、Few-Shot 相关论文整理
深度学习自然语言处理
13+阅读 · 2020年5月22日
Transferring Knowledge across Learning Processes
CreateAMind
6+阅读 · 2019年5月18日
逆强化学习-学习人先验的动机
CreateAMind
5+阅读 · 2019年1月18日
强化学习的Unsupervised Meta-Learning
CreateAMind
7+阅读 · 2019年1月7日
无监督元学习表示学习
CreateAMind
20+阅读 · 2019年1月4日
Unsupervised Learning via Meta-Learning
CreateAMind
27+阅读 · 2019年1月3日
338页新书《Deep Learning in Natural Language Processing》
机器学习算法与Python学习
6+阅读 · 2018年11月6日
Hierarchical Imitation - Reinforcement Learning
CreateAMind
15+阅读 · 2018年5月25日
推荐|深度强化学习聊天机器人(附论文)!
全球人工智能
4+阅读 · 2018年1月30日
Auto-Encoding GAN
CreateAMind
5+阅读 · 2017年8月4日
Top