Tiancheng Zhao


2021

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SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval
Tiancheng Zhao | Xiaopeng Lu | Kyusong Lee
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We introduce SPARTA, a novel neural retrieval method that shows great promise in performance, generalization, and interpretability for open-domain question answering. Unlike many neural ranking methods that use dense vector nearest neighbor search, SPARTA learns a sparse representation that can be efficiently implemented as an Inverted Index. The resulting representation enables scalable neural retrieval that does not require expensive approximate vector search and leads to better performance than its dense counterpart. We validated our approaches on 4 open-domain question answering (OpenQA) tasks and 11 retrieval question answering (ReQA) tasks. SPARTA achieves new state-of-the-art results across a variety of open-domain question answering tasks in both English and Chinese datasets, including open SQuAD, CMRC and etc. Analysis also confirms that the proposed method creates human interpretable representation and allows flexible control over the trade-off between performance and efficiency.

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SF-QA: Simple and Fair Evaluation Library for Open-domain Question Answering
Xiaopeng Lu | Kyusong Lee | Tiancheng Zhao
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

Although open-domain question answering (QA) draws great attention in recent years, it requires large amounts of resources for building the full system and it is often difficult to reproduce previous results due to complex configurations. In this paper, we introduce SF-QA: simple and fair evaluation framework for open-domain QA. SF-QA framework modularizes the pipeline open-domain QA system, which makes the task itself easily accessible and reproducible to research groups without enough computing resources. The proposed evaluation framework is publicly available and anyone can contribute to the code and evaluations.

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VisualSparta: An Embarrassingly Simple Approach to Large-scale Text-to-Image Search with Weighted Bag-of-words
Xiaopeng Lu | Tiancheng Zhao | Kyusong Lee
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Text-to-image retrieval is an essential task in cross-modal information retrieval, i.e., retrieving relevant images from a large and unlabelled dataset given textual queries. In this paper, we propose VisualSparta, a novel (Visual-text Sparse Transformer Matching) model that shows significant improvement in terms of both accuracy and efficiency. VisualSparta is capable of outperforming previous state-of-the-art scalable methods in MSCOCO and Flickr30K. We also show that it achieves substantial retrieving speed advantages, i.e., for a 1 million image index, VisualSparta using CPU gets ~391X speedup compared to CPU vector search and ~5.4X speedup compared to vector search with GPU acceleration. Experiments show that this speed advantage even gets bigger for larger datasets because VisualSparta can be efficiently implemented as an inverted index. To the best of our knowledge, VisualSparta is the first transformer-based text-to-image retrieval model that can achieve real-time searching for large-scale datasets, with significant accuracy improvement compared to previous state-of-the-art methods.

2020

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“None of the Above”: Measure Uncertainty in Dialog Response Retrieval
Yulan Feng | Shikib Mehri | Maxine Eskenazi | Tiancheng Zhao
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

This paper discusses the importance of uncovering uncertainty in end-to-end dialog tasks and presents our experimental results on uncertainty classification on the processed Ubuntu Dialog Corpus. We show that instead of retraining models for this specific purpose, we can capture the original retrieval model’s underlying confidence concerning the best prediction using trivial additional computation.

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Talk to Papers: Bringing Neural Question Answering to Academic Search
Tiancheng Zhao | Kyusong Lee
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

We introduce Talk to Papers, which exploits the recent open-domain question answering (QA) techniques to improve the current experience of academic search. It’s designed to enable researchers to use natural language queries to find precise answers and extract insights from a massive amount of academic papers. We present a large improvement over classic search engine baseline on several standard QA datasets and provide the community a collaborative data collection tool to curate the first natural language processing research QA dataset via a community effort.

2019

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Investigating Evaluation of Open-Domain Dialogue Systems With Human Generated Multiple References
Prakhar Gupta | Shikib Mehri | Tiancheng Zhao | Amy Pavel | Maxine Eskenazi | Jeffrey Bigham
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue

The aim of this paper is to mitigate the shortcomings of automatic evaluation of open-domain dialog systems through multi-reference evaluation. Existing metrics have been shown to correlate poorly with human judgement, particularly in open-domain dialog. One alternative is to collect human annotations for evaluation, which can be expensive and time consuming. To demonstrate the effectiveness of multi-reference evaluation, we augment the test set of DailyDialog with multiple references. A series of experiments show that the use of multiple references results in improved correlation between several automatic metrics and human judgement for both the quality and the diversity of system output.

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Pretraining Methods for Dialog Context Representation Learning
Shikib Mehri | Evgeniia Razumovskaia | Tiancheng Zhao | Maxine Eskenazi
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

This paper examines various unsupervised pretraining objectives for learning dialog context representations. Two novel methods of pretraining dialog context encoders are proposed, and a total of four methods are examined. Each pretraining objective is fine-tuned and evaluated on a set of downstream dialog tasks using the MultiWoz dataset and strong performance improvement is observed. Further evaluation shows that our pretraining objectives result in not only better performance, but also better convergence, models that are less data hungry and have better domain generalizability.

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Target-Guided Open-Domain Conversation
Jianheng Tang | Tiancheng Zhao | Chenyan Xiong | Xiaodan Liang | Eric Xing | Zhiting Hu
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Many real-world open-domain conversation applications have specific goals to achieve during open-ended chats, such as recommendation, psychotherapy, education, etc. We study the problem of imposing conversational goals on open-domain chat agents. In particular, we want a conversational system to chat naturally with human and proactively guide the conversation to a designated target subject. The problem is challenging as no public data is available for learning such a target-guided strategy. We propose a structured approach that introduces coarse-grained keywords to control the intended content of system responses. We then attain smooth conversation transition through turn-level supervised learning, and drive the conversation towards the target with discourse-level constraints. We further derive a keyword-augmented conversation dataset for the study. Quantitative and human evaluations show our system can produce meaningful and effective conversations, significantly improving over other approaches

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Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation
Zhiting Hu | Haoran Shi | Bowen Tan | Wentao Wang | Zichao Yang | Tiancheng Zhao | Junxian He | Lianhui Qin | Di Wang | Xuezhe Ma | Zhengzhong Liu | Xiaodan Liang | Wanrong Zhu | Devendra Sachan | Eric Xing
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

We introduce Texar, an open-source toolkit aiming to support the broad set of text generation tasks that transform any inputs into natural language, such as machine translation, summarization, dialog, content manipulation, and so forth. With the design goals of modularity, versatility, and extensibility in mind, Texar extracts common patterns underlying the diverse tasks and methodologies, creates a library of highly reusable modules and functionalities, and allows arbitrary model architectures and algorithmic paradigms. In Texar, model architecture, inference, and learning processes are properly decomposed. Modules at a high concept level can be freely assembled or plugged in/swapped out. Texar is thus particularly suitable for researchers and practitioners to do fast prototyping and experimentation. The versatile toolkit also fosters technique sharing across different text generation tasks. Texar supports both TensorFlow and PyTorch, and is released under Apache License 2.0 at https://www.texar.io.

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Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models
Tiancheng Zhao | Kaige Xie | Maxine Eskenazi
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)

Defining action spaces for conversational agents and optimizing their decision-making process with reinforcement learning is an enduring challenge. Common practice has been to use handcrafted dialog acts, or the output vocabulary, e.g. in neural encoder decoders, as the action spaces. Both have their own limitations. This paper proposes a novel latent action framework that treats the action spaces of an end-to-end dialog agent as latent variables and develops unsupervised methods in order to induce its own action space from the data. Comprehensive experiments are conducted examining both continuous and discrete action types and two different optimization methods based on stochastic variational inference. Results show that the proposed latent actions achieve superior empirical performance improvement over previous word-level policy gradient methods on both DealOrNoDeal and MultiWoz dialogs. Our detailed analysis also provides insights about various latent variable approaches for policy learning and can serve as a foundation for developing better latent actions in future research.

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Unsupervised Dialog Structure Learning
Weiyan Shi | Tiancheng Zhao | Zhou Yu
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)

Learning a shared dialog structure from a set of task-oriented dialogs is an important challenge in computational linguistics. The learned dialog structure can shed light on how to analyze human dialogs, and more importantly contribute to the design and evaluation of dialog systems. We propose to extract dialog structures using a modified VRNN model with discrete latent vectors. Different from existing HMM-based models, our model is based on variational-autoencoder (VAE). Such model is able to capture more dynamics in dialogs beyond the surface forms of the language. We find that qualitatively, our method extracts meaningful dialog structure, and quantitatively, outperforms previous models on the ability to predict unseen data. We further evaluate the model’s effectiveness in a downstream task, the dialog system building task. Experiments show that, by integrating the learned dialog structure into the reward function design, the model converges faster and to a better outcome in a reinforcement learning setting.

2018

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Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation
Tiancheng Zhao | Kyusong Lee | Maxine Eskenazi
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence representation learning method that can integrate with any existing encoder-decoder dialog models for interpretable response generation. Building upon variational autoencoders (VAEs), we present two novel models, DI-VAE and DI-VST that improve VAEs and can discover interpretable semantics via either auto encoding or context predicting. Our methods have been validated on real-world dialog datasets to discover semantic representations and enhance encoder-decoder models with interpretable generation.

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Texar: A Modularized, Versatile, and Extensible Toolbox for Text Generation
Zhiting Hu | Zichao Yang | Tiancheng Zhao | Haoran Shi | Junxian He | Di Wang | Xuezhe Ma | Zhengzhong Liu | Xiaodan Liang | Lianhui Qin | Devendra Singh Chaplot | Bowen Tan | Xingjiang Yu | Eric Xing
Proceedings of Workshop for NLP Open Source Software (NLP-OSS)

We introduce Texar, an open-source toolkit aiming to support the broad set of text generation tasks. Different from many existing toolkits that are specialized for specific applications (e.g., neural machine translation), Texar is designed to be highly flexible and versatile. This is achieved by abstracting the common patterns underlying the diverse tasks and methodologies, creating a library of highly reusable modules and functionalities, and enabling arbitrary model architectures and various algorithmic paradigms. The features make Texar particularly suitable for technique sharing and generalization across different text generation applications. The toolkit emphasizes heavily on extensibility and modularized system design, so that components can be freely plugged in or swapped out. We conduct extensive experiments and case studies to demonstrate the use and advantage of the toolkit.

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Zero-Shot Dialog Generation with Cross-Domain Latent Actions
Tiancheng Zhao | Maxine Eskenazi
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

This paper introduces zero-shot dialog generation (ZSDG), as a step towards neural dialog systems that can instantly generalize to new situations with minimum data. ZSDG requires an end-to-end generative dialog system to generalize to a new domain for which only a domain description is provided and no training dialogs are available. Then a novel learning framework, Action Matching, is proposed. This algorithm can learn a cross-domain embedding space that models the semantics of dialog responses which in turn, enables a neural dialog generation model to generalize to new domains. We evaluate our methods on two datasets, a new synthetic dialog dataset, and an existing human-human multi-domain dialog dataset. Experimental results show that our method is able to achieve superior performance in learning dialog models that can rapidly adapt their behavior to new domains and suggests promising future research.

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Multimodal Hierarchical Reinforcement Learning Policy for Task-Oriented Visual Dialog
Jiaping Zhang | Tiancheng Zhao | Zhou Yu
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

Creating an intelligent conversational system that understands vision and language is one of the ultimate goals in Artificial Intelligence (AI) (Winograd, 1972). Extensive research has focused on vision-to-language generation, however, limited research has touched on combining these two modalities in a goal-driven dialog context. We propose a multimodal hierarchical reinforcement learning framework that dynamically integrates vision and language for task-oriented visual dialog. The framework jointly learns the multimodal dialog state representation and the hierarchical dialog policy to improve both dialog task success and efficiency. We also propose a new technique, state adaptation, to integrate context awareness in the dialog state representation. We evaluate the proposed framework and the state adaptation technique in an image guessing game and achieve promising results.

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DialCrowd: A toolkit for easy dialog system assessment
Kyusong Lee | Tiancheng Zhao | Alan W. Black | Maxine Eskenazi
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

When creating a dialog system, developers need to test each version to ensure that it is performing correctly. Recently the trend has been to test on large datasets or to ask many users to try out a system. Crowdsourcing has solved the issue of finding users, but it presents new challenges such as how to use a crowdsourcing platform and what type of test is appropriate. DialCrowd has been designed to make system assessment easier and to ensure the quality of the result. This paper describes DialCrowd, what specific needs it fulfills and how it works. It then relates a test of DialCrowd by a group of dialog system developer.

2017

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Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability
Tiancheng Zhao | Allen Lu | Kyusong Lee | Maxine Eskenazi
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

Generative encoder-decoder models offer great promise in developing domain-general dialog systems. However, they have mainly been applied to open-domain conversations. This paper presents a practical and novel framework for building task-oriented dialog systems based on encoder-decoder models. This framework enables encoder-decoder models to accomplish slot-value independent decision-making and interact with external databases. Moreover, this paper shows the flexibility of the proposed method by interleaving chatting capability with a slot-filling system for better out-of-domain recovery. The models were trained on both real-user data from a bus information system and human-human chat data. Results show that the proposed framework achieves good performance in both offline evaluation metrics and in task success rate with human users.

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DialPort, Gone Live: An Update After A Year of Development
Kyusong Lee | Tiancheng Zhao | Yulun Du | Edward Cai | Allen Lu | Eli Pincus | David Traum | Stefan Ultes | Lina M. Rojas-Barahona | Milica Gasic | Steve Young | Maxine Eskenazi
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

DialPort collects user data for connected spoken dialog systems. At present six systems are linked to a central portal that directs the user to the applicable system and suggests systems that the user may be interested in. User data has started to flow into the system.

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Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders
Tiancheng Zhao | Ran Zhao | Maxine Eskenazi
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

While recent neural encoder-decoder models have shown great promise in modeling open-domain conversations, they often generate dull and generic responses. Unlike past work that has focused on diversifying the output of the decoder from word-level to alleviate this problem, we present a novel framework based on conditional variational autoencoders that capture the discourse-level diversity in the encoder. Our model uses latent variables to learn a distribution over potential conversational intents and generates diverse responses using only greedy decoders. We have further developed a novel variant that is integrated with linguistic prior knowledge for better performance. Finally, the training procedure is improved through introducing a bag-of-word loss. Our proposed models have been validated to generate significantly more diverse responses than baseline approaches and exhibit competence of discourse-level decision-making.

2016

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Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning
Tiancheng Zhao | Maxine Eskenazi
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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DialPort: A General Framework for Aggregating Dialog Systems
Tiancheng Zhao | Kyusong Lee | Maxine Eskenazi
Proceedings of the Workshop on Uphill Battles in Language Processing: Scaling Early Achievements to Robust Methods

2015

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An Incremental Turn-Taking Model with Active System Barge-in for Spoken Dialog Systems
Tiancheng Zhao | Alan W Black | Maxine Eskenazi
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue