Collecting data for training dialog systems can be extremely expensive due to the involvement of human participants and the need for extensive annotation.Especially in documentgrounded dialog systems, human experts need to carefully read the unstructured documents to answer the users' questions.As a result, existing document-grounded dialog datasets are relatively small-scale and obstruct the effective training of dialogue systems.In this paper, we propose an automatic data augmentation technique grounded on documents through a generative dialogue model.The dialogue model consists of a user bot and agent bot that can synthesize diverse dialogues given an input document, which are then used to train a downstream model.When supplementing the original dataset, our method achieves significant improvement over traditional data augmentation methods.We also achieve competitive performance in the low-resource setting.

We propose MultiDoc2Dial, a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents.Most previous works treat document-grounded dialogue modeling as a machine reading comprehension task based on a single given document or passage.In this work, we aim to address more realistic scenarios where a goaloriented information-seeking conversation involves multiple topics, and hence is grounded on different documents.To facilitate such a task, we introduce a new dataset that contains dialogues grounded in multiple documents from four different domains.We also explore modeling the dialogue-based and documentbased context in the dataset.We present strong baseline approaches and various experimental results, aiming to support further research efforts on such a task. Social Security CreditsYou must earn at least 40 Social Security credits to qualify for social security benefits. Number of Credit Needed for Disability BenefitsTo be eligible for disability benefits, you must meet a recent work test and a duration work test.Number of Credit Needed for Retirement Benefits If you are born after 1928, you will need 40 credits to qualify for retirement benefits.30 years or older -In general, you must have at least 20 credits in the 10-year period immediately before you become disabled.U1: I need help with SSDI.I heard that it could benefit my relatives too.I am in my 50s.A2: Yes SSDI pays benefits to you and

Hanjie Chen, Song Feng, Jatin Ganhotra, Hui Wan, Chulaka Gunasekara, Sachindra Joshi, Yangfeng Ji. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

Hui Wan, Song Feng, Chulaka Gunasekara, Siva Sankalp Patel, Sachindra Joshi, Luis Lastras. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

We introduce Doc2Dial, an end-to-end framework for generating conversational data grounded in given documents. It takes the documents as input and generates the pipelined tasks for obtaining the annotations specifically for producing the simulated dialog flows. Then, the dialog flows are used to guide the collection of the utterances via the integrated crowdsourcing tool. The outcomes include the human-human dialogue data grounded in the given documents, as well as various types of automatically or human labeled annotations that help ensure the quality of the dialog data with the flexibility to (re)composite dialogues. We expect such data can facilitate building automated dialogue agents for goal-oriented tasks. We demonstrate Doc2Dial system with the various domain documents for customer care.

Implicit relation classification onPenn Discourse TreeBank (PDTB) 2.0 is a common benchmark task for evaluating the understanding of discourse relations.However, the lack of consistency in preprocessing and evaluation poses challenges to fair comparison of results in the literature.In this work, we highlight these inconsistencies and propose an improved evaluation protocol.Paired with this protocol, we report strong baseline results from pretrained sentence encoders, which set the new state-of-the-art for PDTB 2.0.Furthermore, this work is the first to explore fine-grained relation classification on PDTB 3.0.We expect our work to serve as a point of comparison for future work, and also as an initiative to discuss models of larger context and possible data augmentations for downstream transferability.

We introduce doc2dial, a new dataset of goal-oriented dialogues that are grounded in the associated documents.Inspired by how the authors compose documents for guiding end users, we first construct dialogue flows based on the content elements that corresponds to higher-level relations across text sections as well as lower-level relations between discourse units within a section.Then we present these dialogue flows to crowd contributors to create conversational utterances.The dataset includes over 4500 annotated conversations with an average of 14 turns that are grounded in over 450 documents from four domains.Compared to the prior document-grounded dialogue datasets, this dataset covers a variety of dialogue scenes in information-seeking conversations.For evaluating the versatility of the dataset, we introduce multiple dialogue modeling tasks and present baseline approaches.

In this paper, we propose Text2Scene, a model that generates various forms of compositional scene representations from natural language descriptions. Unlike recent works, our method does NOT use Generative Adversarial Networks (GANs). Text2Scene instead learns to sequentially generate objects and their attributes (location, size, appearance, etc) at every time step by attending to different parts of the input text and the current status of the generated scene. We show that under minor modifications, the proposed framework can handle the generation of different forms of scene representations, including cartoon-like scenes, object layouts corresponding to real images, and synthetic images. Our method is not only competitive when compared with state-of-the-art GAN-based methods using automatic metrics and superior based on human judgments but also has the advantage of producing interpretable results.

This paper explores the task of interactive image retrieval using natural language queries, where a user progressively provides input queries to refine a set of retrieval results. Moreover, our work explores this problem in the context of complex image scenes containing multiple objects. We propose Drill-down, an effective framework for encoding multiple queries with an efficient compact state representation that significantly extends current methods for single-round image retrieval. We show that using multiple rounds of natural language queries as input can be surprisingly effective to find arbitrarily specific images of complex scenes. Furthermore, we find that existing image datasets with textual captions can provide a surprisingly effective form of weak supervision for this task. We compare our method with existing sequential encoding and embedding networks, demonstrating superior performance on two proposed benchmarks: automatic image retrieval on a simulated scenario that uses region captions as queries, and interactive image retrieval using real queries from human evaluators.

We propose a unified Implicit Dialog framework for goal-oriented, information seeking tasks of Conversational Commerce applications. It aims to enable the dialog interactions with domain data without replying on the explicitly encoded rules but utilizing the underlying data representation to build the components required for the interactions, which we refer as Implicit Dialog in this work. The proposed framework consists of a pipeline of End-to-End trainable modules. It generates a centralized knowledge representation to semantically ground multiple sub-modules. The framework is also integrated with an associated set of tools to gather end users' input for continuous improvement of the system. This framework is designed to facilitate fast development of conversational systems by identifying the components and the data that can be adapted and reused across many end-user applications. We demonstrate our approach by creating conversational agents for several independent domains.

We study Refer-to-as relations as a new type of semanticknowledge. Compared to the much studied Is-a relation,which concerns factual taxonomy knowledge, Refer-to-as relationsaim to address pragmatic semantic knowledge. Forexample, a “penguin” is a “bird” from a taxonomy point ofview, but people rarely refer to a “penguin” as a “bird” invernacular use. This observation closely relates to the entrylevelcategorization studied in Prototype Theory in Psychology.We posit that Refer-to-as relations can be learned fromdata, and that both textual and visual information would behelpful in inferring the relations. By integrating existing lexicalstructure knowledge with language statistics and visualsimilarities, we formulate a collective inference approach tomap all object names in an encyclopedia to commonly usednames for each object. Our contributions include a new labeleddata set, the inference and optimization approach, andthe computed mappings and similarities.

We introduce ConnotationWordNet, a connotation lexicon over the network of words in conjunction with senses.We formulate the lexicon induction problem as collective inference over pairwise-Markov Random Fields, and present a loopy belief propagation algorithm for inference.The key aspect of our method is that it is the first unified approach that assigns the polarity of both word-and sense-level connotations, exploiting the innate bipartite graph structure encoded in WordNet.We present comprehensive evaluation to demonstrate the quality and utility of the resulting lexicon in comparison to existing connotation and sentiment lexicons.

In this paper, we explore the use of keyboard strokes as a means to access the real-time writ-ing process of online authors, analogously to prosody in speech analysis, in the context of deception detection. We show that differences in keystroke patterns like editing maneuvers and duration of pauses can help distinguish be-tween truthful and deceptive writing. Empiri-cal results show that incorporating keystroke-based features lead to improved performance in deception detection in two different do-mains: online reviews and essays. 1

Predicting the success of literary works is a curious question among publishers and aspiring writers alike.We examine the quantitative connection, if any, between writing style and successful literature.Based on novels over several different genres, we probe the predictive power of statistical stylometry in discriminating successful literary works, and identify characteristic stylistic elements that are more prominent in successful writings.Our study reports for the first time that statistical stylometry can be surprisingly effective in discriminating highly successful literature from less successful counterpart, achieving accuracy up to 84%.Closer analyses lead to several new insights into characteristics of the writing style in successful literature, including findings that are contrary to the conventional wisdom with respect to good writing style and readability.

This demo will present HearSay, a multi-modal non-visual web browser, which aims to bridge the growing Web Accessibility divide between individuals with visual impairments and their sighted counterparts, and to facilitate full participation of blind individuals in the growing Web-based society.