Jindong (JD) Chen
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RewriteLM: An Instruction-Tuned Large LanguageModel for Text Rewriting
Liangchen Luo
Yun Zhu
Simon Tong
Lei Meng
Proceedings of the AAAI Conference on Artificial Intelligence, 38(17), 18970-18980 (2024)
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In recent years, Large Language Models (LLMs) have demonstrated impressive zero-shot capabilities in text generation tasks expressed through natural language instructions. However, text rewriting is a challenging task, and unintended modifications can negatively impact the system's performance. To address this challenge, we introduce a novel benchmark for text rewriting that covers a wide variety of rewriting types expressed through natural language instructions. Unlike previous benchmarks, which were primarily focused on limited rewrite styles and sentence-level rewriting, our benchmark is specifically designed to facilitate open-ended rewriting of long-form text. Additionally, we present a strong baseline model, RewriteLM, which is an instruction-tuned large language model for text rewriting. The model is trained using supervised fine-tuning, reward training, and reinforcement learning. To minimize human intervention in the data collection process, we develop new data generation strategies: (1) utilizing high-quality, long-form edits from Wikipedia as our primary natural training data source, (2) generating a synthetic dataset that includes diverse edit types and non-Wiki domains using chain-of-thoughts and the capabilities of LLMs, and (3) employing human-designed heuristic rankers to generate preference data. Our experiments demonstrate the effectiveness of our proposed benchmark and baseline model, as well as the benefits of our data collection strategies in minimizing human intervention.
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We present a new task and dataset, ScreenQA, for screen content understanding via question answering. The existing screen datasets are focused either on structure and component-level understanding, or on a much higher-level composite task such as navigation and task completion. We attempt to bridge the gap between these two by annotating 86K question-answer pairs over the RICO dataset in hope to benchmark the screen reading comprehension capacity.
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ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Gilles Baechler
Srinivas Sunkara
Maria Wang
Hassan Mansoor
Vincent Etter
Jason Lin
(2024)
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Screen user interfaces (UIs) and infographics, sharing similar visual language and design principles, play important roles in human communication and human-machine interaction. We introduce ScreenAI, a vision-language model that specializes in UI and infographics understanding. Our model improves upon the PaLI architecture with the flexible patching strategy of pix2struct and is trained on a unique mixture of datasets. At the heart of this mixture is a novel screen annotation task in which the model has to identify the type and location of UI elements. We use these text annotations to describe screens to Large Language Models and automatically generate question-answering (QA), UI navigation, and summarization training datasets at scale. We run ablation studies to demonstrate the impact of these design choices. At only 5B parameters, ScreenAI achieves new state-of-the-artresults on UI- and infographics-based tasks (Multi-page DocVQA, WebSRC, MoTIF and Widget Captioning), and new best-in-class performance on others (Chart QA, DocVQA, and InfographicVQA) compared to models of similar size. Finally, we release three new datasets: one focused on the screen annotation task and two others focused on question answering.
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We present a new human-human dialogue dataset - PhotoChat, the first dataset that casts light on the photo sharing behavior in online messaging. PhotoChat contains 12k dialogues, each of which is paired with a user photo that is shared during the conversation. Based on this dataset, we propose two tasks to facilitate research on image-text modeling: a photo-sharing intent prediction task that predicts whether one intends to share a photo in the next conversation turn, and a photo retrieval task that retrieves the most relevant photo according to the dialogue context. In addition, for both tasks, we provide baseline models using the state-of-the-art models and report their benchmark performances. The best image retrieval model achieves 10.4% recall@1 (out of 1000 candidates) and the best photo intent prediction model achieves 58.1% F1 score, indicating that the dataset presents interesting yet challenging real-world problems. We are releasing PhotoChat to facilitate future research work among the community.
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UIBert: Learning Generic Multimodal Representations for UI Understanding
Chongyang Bai
Srinivas Kumar Sunkara
Xiaoxue Zang
Ying Xu
the 30th International Joint Conference on Artificial Intelligence (IJCAI-21) (2021)
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To improve the accessibility of smart devices and to simplify their usage, building models which understand user interfaces (UIs) and assist users to complete their tasks is critical. However, unique challenges are proposed by UI-specific characteristics, such as how to effectively leverage multimodal UI features that involve image, text, and structural metadata and how to achieve good performance when high-quality labeled data is unavailable. To address such challenges we introduce UIBert, a transformer-based joint image-text model trained through novel pre-training tasks on large-scale unlabeled UI data to learn generic feature representations for a UI and its components. Our key intuition is that the heterogeneous features in a UI are self-aligned, i.e., the image and text features of UI components, are predictive of each other. We propose five pretraining tasks utilizing this self-alignment among different features of a UI component and across various components in the same UI. We evaluate our method on nine real-world downstream UI tasks where UIBert outperforms strong multimodal baselines by up to 9.26% accuracy.
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ActionBert: Leveraging User Actions for Semantic Understanding of User Interfaces
Zecheng He
Srinivas Sunkara
Xiaoxue Zang
Ying Xu
Gabriel Schubiner
Ruby Lee
AAAI-21 (2020)
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As mobile devices are becoming ubiquitous, regularly interacting with a variety of user interfaces (UIs) is a common
aspect of daily life for many people. To improve the accessibility of these devices and to enable their usage in a variety
of settings, building models that can assist users and accomplish tasks through the UI is vitally important. However, there
are several challenges to achieve this. First, UI components of
similar appearance can have different functionalities, making
understanding their function more important than just analyzing their appearance. Second, domain-specific features like
Document Object Model (DOM) in web pages and View Hierarchy (VH) in mobile applications provide important signals about the semantics of UI elements, but these features
are not in a natural language format. Third, owing to a large
diversity in UIs and absence of standard DOM or VH representations, building a UI understanding model with high coverage requires large amounts of training data.
Inspired by the success of pre-training based approaches in
NLP for tackling a variety of problems in a data-efficient
way, we introduce a new pre-trained UI representation model
called ActionBert. Our methodology is designed to leverage
visual, linguistic and domain-specific features in user interaction traces to pre-train generic feature representations of UIs
and their components. Our key intuition is that user actions,
e.g., a sequence of clicks on different UI components, reveals
important information about their functionality. We evaluate
the proposed model on a wide variety of downstream tasks,
ranging from icon classification to UI component retrieval
based on its natural language description. Experiments show
that the proposed ActionBert model outperforms multi-modal
baselines across all downstream tasks by up to 15.5%.
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MultiWOZ 2.2 : A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines
Xiaoxue Zang
Srinivas Sunkara
Jianguo Zhang
Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI (2020), pp. 109-117
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MultiWOZ is a well-known task-oriented dialogue dataset containing over 10,000 annotated dialogues spanning 8 domains. It is extensively used as a benchmark for dialogue state tracking. However, recent works have reported presence of substantial noise in the dialogue state annotations. MultiWOZ 2.1 identified and fixed many of these erroneous annotations and user utterances, resulting in an improved version of this dataset. This work introduces MultiWOZ 2.2, which is a yet another improved version of this dataset. Firstly, we identify and fix dialogue state annotation errors across 17.3% of the utterances on top of MultiWOZ 2.1. Secondly, we redefine the ontology by disallowing vocabularies of slots with a large number of possible values (e.g., restaurant name, time of booking). In addition, we introduce slot span annotations for these slots to standardize them across recent models, which previously used custom string matching heuristics to generate them. We also benchmark a few state of the art dialogue state tracking models on the corrected dataset to facilitate comparison for future work. In the end, we discuss best practices for dialogue data collection that can help avoid annotation errors.
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Learning Question-Guided Video Representation for Multi-Turn Video Question Answering
Guan-Lin Chao
Semih Yavuz
Dilek Hakkani-Tur
Ian Lane
Proceedings of SIGdial (2019) (to appear)
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Understanding and conversing about dynamic scenes is one of the key capabilities of AI agents that navigate the environment and convey useful information to humans. Video question answering is a specific scenario of such AI-human interaction where an agent generates a natural language response to a question regarding the video of a dynamic scene. Incorporating features from multiple modalities, which often provide supplementary information, is one of the challenging aspects of video question answering. Furthermore, a question often concerns only a small segment of the video, hence encoding the entire video sequence using a recurrent neural network is not computationally efficient. Our proposed question-guided video representation module efficiently generates the token-level video summary guided by each word in the question. The learned representations are then fused with the question to generate the answer. Through empirical evaluation on the Audio Visual Scene-aware Dialog (AVSD) dataset (Alamri et al., 2019a), our proposed models in single-turn and multiturn question answering achieve state-of-theart performance on several automatic natural language generation evaluation metrics.
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Images may have elements containing text and a bounding box associated with them, for example, text identified via optical character recognition on a computer screen image, or a natural image with labeled objects. We present an end-to-end trainable architecture to incorporate the information from these elements and the image to segment/identify the part of the image a natural language expression is referring to. We calculate an embedding for each element and then project it onto the corresponding location (i.e., the associated bounding box) of the image feature map. We show that this architecture gives an improvement in resolving referring expressions, over only using the image, and other methods that incorporate the element information. We demonstrate experimental results on the referring expression datasets based on COCO, and on a webpage image referring expression dataset that we developed.
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