Tagyoung Chung


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Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems
Anish Acharya | Suranjit Adhikari | Sanchit Agarwal | Vincent Auvray | Nehal Belgamwar | Arijit Biswas | Shubhra Chandra | Tagyoung Chung | Maryam Fazel-Zarandi | Raefer Gabriel | Shuyang Gao | Rahul Goel | Dilek Hakkani-Tur | Jan Jezabek | Abhay Jha | Jiun-Yu Kao | Prakash Krishnan | Peter Ku | Anuj Goyal | Chien-Wei Lin | Qing Liu | Arindam Mandal | Angeliki Metallinou | Vishal Naik | Yi Pan | Shachi Paul | Vittorio Perera | Abhishek Sethi | Minmin Shen | Nikko Strom | Eddie Wang
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations

Traditional goal-oriented dialogue systems rely on various components such as natural language understanding, dialogue state tracking, policy learning and response generation. Training each component requires annotations which are hard to obtain for every new domain, limiting scalability of such systems. Similarly, rule-based dialogue systems require extensive writing and maintenance of rules and do not scale either. End-to-End dialogue systems, on the other hand, do not require module-specific annotations but need a large amount of data for training. To overcome these problems, in this demo, we present Alexa Conversations, a new approach for building goal-oriented dialogue systems that is scalable, extensible as well as data efficient. The components of this system are trained in a data-driven manner, but instead of collecting annotated conversations for training, we generate them using a novel dialogue simulator based on a few seed dialogues and specifications of APIs and entities provided by the developer. Our approach provides out-of-the-box support for natural conversational phenomenon like entity sharing across turns or users changing their mind during conversation without requiring developers to provide any such dialogue flows. We exemplify our approach using a simple pizza ordering task and showcase its value in reducing the developer burden for creating a robust experience. Finally, we evaluate our system using a typical movie ticket booking task integrated with live APIs and show that the dialogue simulator is an essential component of the system that leads to over 50% improvement in turn-level action signature prediction accuracy.

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Style Control for Schema-Guided Natural Language Generation
Alicia Tsai | Shereen Oraby | Vittorio Perera | Jiun-Yu Kao | Yuheng Du | Anjali Narayan-Chen | Tagyoung Chung | Dilek Hakkani-Tur
Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI

Natural Language Generation (NLG) for task-oriented dialogue systems focuses on communicating specific content accurately, fluently, and coherently. While these attributes are crucial for a successful dialogue, it is also desirable to simultaneously accomplish specific stylistic goals, such as response length, point-of-view, descriptiveness, sentiment, formality, and empathy. In this work, we focus on stylistic control and evaluation for schema-guided NLG, with joint goals of achieving both semantic and stylistic control. We experiment in detail with various controlled generation methods for large pretrained language models: specifically, conditional training, guided fine-tuning, and guided decoding. We discuss their advantages and limitations, and evaluate them with a broad range of automatic and human evaluation metrics. Our results show that while high style accuracy and semantic correctness are easier to achieve for more lexically-defined styles with conditional training, stylistic control is also achievable for more semantically complex styles using discriminator-based guided decoding methods. The results also suggest that methods that are more scalable (with less hyper-parameters tuning) and that disentangle context generation and stylistic variations are more effective at achieving semantic correctness and style accuracy.

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Few Shot Dialogue State Tracking using Meta-learning
Saket Dingliwal | Shuyang Gao | Sanchit Agarwal | Chien-Wei Lin | Tagyoung Chung | Dilek Hakkani-Tur
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Dialogue State Tracking (DST) forms a core component of automated chatbot based systems designed for specific goals like hotel, taxi reservation, tourist information etc. With the increasing need to deploy such systems in new domains, solving the problem of zero/few-shot DST has become necessary. There has been a rising trend for learning to transfer knowledge from resource-rich domains to unknown domains with minimal need for additional data. In this work, we explore the merits of meta-learning algorithms for this transfer and hence, propose a meta-learner D-REPTILE specific to the DST problem. With extensive experimentation, we provide clear evidence of benefits over conventional approaches across different domains, methods, base models and datasets with significant (5-25%) improvement over the baseline in a low-data setting. Our proposed meta-learner is agnostic of the underlying model and hence any existing state-of-the-art DST system can improve its performance on unknown domains using our training strategy.


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Schema-Guided Natural Language Generation
Yuheng Du | Shereen Oraby | Vittorio Perera | Minmin Shen | Anjali Narayan-Chen | Tagyoung Chung | Anushree Venkatesh | Dilek Hakkani-Tur
Proceedings of the 13th International Conference on Natural Language Generation

Neural network based approaches to data-to-text natural language generation (NLG) have gained popularity in recent years, with the goal of generating a natural language prompt that accurately realizes an input meaning representation. To facilitate the training of neural network models, researchers created large datasets of paired utterances and their meaning representations. However, the creation of such datasets is an arduous task and they mostly consist of simple meaning representations composed of slot and value tokens to be realized. These representations do not include any contextual information that an NLG system can use when trying to generalize, such as domain information and descriptions of slots and values. In this paper, we present the novel task of Schema-Guided Natural Language Generation (SG-NLG). Here, the goal is still to generate a natural language prompt, but in SG-NLG, the input MRs are paired with rich schemata providing contextual information. To generate a dataset for SG-NLG we re-purpose an existing dataset for another task: dialog state tracking, which includes a large and rich schema spanning multiple different attributes, including information about the domain, user intent, and slot descriptions. We train different state-of-the-art models for neural natural language generation on this dataset and show that in many cases, including rich schema information allows our models to produce higher quality outputs both in terms of semantics and diversity. We also conduct experiments comparing model performance on seen versus unseen domains, and present a human evaluation demonstrating high ratings for overall output quality.

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From Machine Reading Comprehension to Dialogue State Tracking: Bridging the Gap
Shuyang Gao | Sanchit Agarwal | Di Jin | Tagyoung Chung | Dilek Hakkani-Tur
Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI

Dialogue state tracking (DST) is at the heart of task-oriented dialogue systems. However, the scarcity of labeled data is an obstacle to building accurate and robust state tracking systems that work across a variety of domains. Existing approaches generally require some dialogue data with state information and their ability to generalize to unknown domains is limited. In this paper, we propose using machine reading comprehension (RC) in state tracking from two perspectives: model architectures and datasets. We divide the slot types in dialogue state into categorical or extractive to borrow the advantages from both multiple-choice and span-based reading comprehension models. Our method achieves near the current state-of-the-art in joint goal accuracy on MultiWOZ 2.1 given full training data. More importantly, by leveraging machine reading comprehension datasets, our method outperforms the existing approaches by many a large margin in few-shot scenarios when the availability of in-domain data is limited. Lastly, even without any state tracking data, i.e., zero-shot scenario, our proposed approach achieves greater than 90% average slot accuracy in 12 out of 30 slots in MultiWOZ 2.1.


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Simple Question Answering with Subgraph Ranking and Joint-Scoring
Wenbo Zhao | Tagyoung Chung | Anuj Goyal | Angeliki Metallinou
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Knowledge graph based simple question answering (KBSQA) is a major area of research within question answering. Although only dealing with simple questions, i.e., questions that can be answered through a single knowledge base (KB) fact, this task is neither simple nor close to being solved. Targeting on the two main steps, subgraph selection and fact selection, the literature has developed sophisticated approaches. However, the importance of subgraph ranking and leveraging the subject–relation dependency of a KB fact have not been sufficiently explored. Motivated by this, we present a unified framework to describe and analyze existing approaches. Using this framework as a starting point we focus on two aspects: improving subgraph selection through a novel ranking method, and leveraging the subject–relation dependency by proposing a joint scoring CNN model with a novel loss function that enforces the well-order of scores. Our methods achieve a new state of the art (85.44% in accuracy) on the SimpleQuestions dataset.

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Practical Semantic Parsing for Spoken Language Understanding
Marco Damonte | Rahul Goel | Tagyoung Chung
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)

Executable semantic parsing is the task of converting natural language utterances into logical forms that can be directly used as queries to get a response. We build a transfer learning framework for executable semantic parsing. We show that the framework is effective for Question Answering (Q&A) as well as for Spoken Language Understanding (SLU). We further investigate the case where a parser on a new domain can be learned by exploiting data on other domains, either via multi-task learning between the target domain and an auxiliary domain or via pre-training on the auxiliary domain and fine-tuning on the target domain. With either flavor of transfer learning, we are able to improve performance on most domains; we experiment with public data sets such as Overnight and NLmaps as well as with commercial SLU data. The experiments carried out on data sets that are different in nature show how executable semantic parsing can unify different areas of NLP such as Q&A and SLU.

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Dialog State Tracking: A Neural Reading Comprehension Approach
Shuyang Gao | Abhishek Sethi | Sanchit Agarwal | Tagyoung Chung | Dilek Hakkani-Tur
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue

Dialog state tracking is used to estimate the current belief state of a dialog given all the preceding conversation. Machine reading comprehension, on the other hand, focuses on building systems that read passages of text and answer questions that require some understanding of passages. We formulate dialog state tracking as a reading comprehension task to answer the question what is the state of the current dialog? after reading conversational context. In contrast to traditional state tracking methods where the dialog state is often predicted as a distribution over a closed set of all the possible slot values within an ontology, our method uses a simple attention-based neural network to point to the slot values within the conversation. Experiments on MultiWOZ-2.0 cross-domain dialog dataset show that our simple system can obtain similar accuracies compared to the previous more complex methods. By exploiting recent advances in contextual word embeddings, adding a model that explicitly tracks whether a slot value should be carried over to the next turn, and combining our method with a traditional joint state tracking method that relies on closed set vocabulary, we can obtain a joint-goal accuracy of 47.33% on the standard test split, exceeding current state-of-the-art by 11.75%**.

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Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators
Sanghyun Yi | Rahul Goel | Chandra Khatri | Alessandra Cervone | Tagyoung Chung | Behnam Hedayatnia | Anu Venkatesh | Raefer Gabriel | Dilek Hakkani-Tur
Proceedings of the 12th International Conference on Natural Language Generation

Encoder-decoder based neural architectures serve as the basis of state-of-the-art approaches in end-to-end open domain dialog systems. Since most of such systems are trained with a maximum likelihood (MLE) objective they suffer from issues such as lack of generalizability and the generic response problem, i.e., a system response that can be an answer to a large number of user utterances, e.g., “Maybe, I don’t know.” Having explicit feedback on the relevance and interestingness of a system response at each turn can be a useful signal for mitigating such issues and improving system quality by selecting responses from different approaches. Towards this goal, we present a system that evaluates chatbot responses at each dialog turn for coherence and engagement. Our system provides explicit turn-level dialog quality feedback, which we show to be highly correlated with human evaluation. To show that incorporating this feedback in the neural response generation models improves dialog quality, we present two different and complementary mechanisms to incorporate explicit feedback into a neural response generation model: reranking and direct modification of the loss function during training. Our studies show that a response generation model that incorporates these combined feedback mechanisms produce more engaging and coherent responses in an open-domain spoken dialog setting, significantly improving the response quality using both automatic and human evaluation.


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The Alexa Meaning Representation Language
Thomas Kollar | Danielle Berry | Lauren Stuart | Karolina Owczarzak | Tagyoung Chung | Lambert Mathias | Michael Kayser | Bradford Snow | Spyros Matsoukas
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)

This paper introduces a meaning representation for spoken language understanding. The Alexa meaning representation language (AMRL), unlike previous approaches, which factor spoken utterances into domains, provides a common representation for how people communicate in spoken language. AMRL is a rooted graph, links to a large-scale ontology, supports cross-domain queries, fine-grained types, complex utterances and composition. A spoken language dataset has been collected for Alexa, which contains ∼20k examples across eight domains. A version of this meaning representation was released to developers at a trade show in 2016.


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Sampling Tree Fragments from Forests
Tagyoung Chung | Licheng Fang | Daniel Gildea | Daniel Štefankovič
Computational Linguistics, Volume 40, Issue 1 - March 2014


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Tuning as Linear Regression
Marzieh Bazrafshan | Tagyoung Chung | Daniel Gildea
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Direct Error Rate Minimization for Statistical Machine Translation
Tagyoung Chung | Michel Galley
Proceedings of the Seventh Workshop on Statistical Machine Translation


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Terminal-Aware Synchronous Binarization
Licheng Fang | Tagyoung Chung | Daniel Gildea
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Issues Concerning Decoding with Synchronous Context-free Grammar
Tagyoung Chung | Licheng Fang | Daniel Gildea
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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SCFG latent annotation for machine translation
Tagyoung Chung | Licheng Fang | Daniel Gildea
Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign

We discuss learning latent annotations for synchronous context-free grammars (SCFG) for the purpose of improving machine translation. We show that learning annotations for nonterminals results in not only more accurate translation, but also faster SCFG decoding.


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Effects of Empty Categories on Machine Translation
Tagyoung Chung | Daniel Gildea
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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Factors Affecting the Accuracy of Korean Parsing
Tagyoung Chung | Matt Post | Daniel Gildea
Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages


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Unsupervised Tokenization for Machine Translation
Tagyoung Chung | Daniel Gildea
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing