Different Open Information Extraction (OIE)tasks require different types of information, so the OIE field requires strong adaptability of OIE algorithms to meet different task requirements.This paper discusses the adaptability problem in existing OIE systems and designs a new adaptable and efficient OIE system -OIE@OIA as a solution.OIE@OIA follows the methodology of Open Information eXpression (OIX): parsing a sentence to an Open Information Annotation (OIA) Graph and then adapting the OIA graph to different OIE tasks with simple rules.As the core of our OIE@OIA system, we implement an endto-end OIA generator by annotating a dataset (we make it open available) and designing an efficient learning algorithm for the complex OIA graph.We easily adapt the OIE@OIA system to accomplish three popular OIE tasks.The experimental show that our OIE@OIA achieves new SOTA performances on these tasks, showing the great adaptability of our OIE@OIA system.Furthermore, compared to other end-to-end OIE baselines that need millions of samples for training, our OIE@OIA needs much fewer training samples (12K), showing a significant advantage in terms of efficiency.

Click-through rate (CTR) prediction is crucial for online sponsored search advertising. Several successful CTR models have been adopted in the industry, including the regularized logistic regression (LR). Nonetheless, the learning process suffers from two limitations: 1) Feature crosses for high-order information may generate trillions of features, which are sparse for online learning examples; 2) Rapid changing of data distribution brings challenges to the accurate learning since the model has to perform a fast adaptation on the new data. Moreover, existing adaptive optimizers are ineffective in handling the sparsity issue for high-dimensional features.

Existing OIE (Open Information Extraction) algorithms are independent of each other such that there exist lots of redundant works; the featured strategies are not reusable and not adaptive to new tasks. This paper proposes a new pipeline to build OIE systems, where an Open-domain Information eXpression (OIX) task is proposed to provide a platform for all OIE strategies. The OIX is an OIE friendly expression of a sentence without information loss. The generation procedure of OIX contains shared works of OIE algorithms so that OIE strategies can be developed on the platform of OIX as inference operations focusing on more critical problems. Based on the same platform of OIX, the OIE strategies are reusable, and people can select a set of strategies to assemble their algorithm for a specific task so that the adaptability may be significantly increased. This paper focuses on the task of OIX and propose a solution – Open Information Annotation (OIA). OIA is a predicate-function-argument annotation for sentences. We label a data set of sentence-OIA pairs and propose a dependency-based rule system to generate OIA annotations from sentences. The evaluation results reveal that learning the OIA from a sentence is a challenge owing to the complexity of natural language sentences, and it is worthy of attracting more attention from the research community.

Recent years have witnessed the continuing growth of people’s dependence on touchscreen devices. As a result, input speed with the onscreen keyboard has become crucial to communication efficiency and user experience. In this work, we formally discuss the general problem of input expectation prediction with a touch-screen input method editor (IME). Taken input efficiency as the optimization target, we proposed a neural end-to-end candidates generation solution to handle automatic correction, reordering, insertion, deletion as well as completion. Evaluation metrics are also discussed base on real use scenarios. For a more thorough comparison, we also provide a statistical strategy for mapping touch coordinate sequences to text input candidates. The proposed model and baselines are evaluated on a real-world dataset. The experiment (conducted on the PaddlePaddle deep learning platform1) shows that the proposed model outperforms the baselines.

Mobile devices (e.g., smartphones) play a crucial role in our daily lives nowadays. People rely heavily on mobile devices for searching online, sending emails, chatting with friends, etc. As a result, input efficiency becomes increasingly important for real-time communication on mobile devices. Due to the small size of the screen on mobile devices, however, it is oftentimes frustrating for users to correct or update the input sequences on an even smaller input area on the screen. This often causes poor user experience. In this paper, we focus on improving the input efficiency on mobile devices to offer better user experience. In order to achieve efficient input, there are multiple challenges: 1) how to employ a single, unified representation of the keyboard layouts for different input languages; 2) how to build a framework to correct a mistouch immediately and predict the coming input texts (words or phrases) effectively; 3) how to deploy and evaluate the model on mobile devices with limited computational power. To address these challenges, we introduce \em FastInput to improve the user input efficiency on mobile devices. Three key techniques are developed in FastInput -- layout modeling, instant mistouch correction and user input text prediction. We also design solutions for efficient deployment and evaluation of FastInput on mobile devices. The proposed FastInput achieves higher efficiency compared to the traditional input system over millions of user input sequences in different languages.

In this paper, we consider the problem of open information extraction (OIE) for extracting entity and relation level intermediate structures from sentences in open-domain. We focus on four types of valuable intermediate structures (Relation, Attribute, Description, and Concept), and propose a unified knowledge expression form, SAOKE, to express them. We publicly release a data set which contains 48,248 sentences and the corresponding facts in the SAOKE format labeled by crowdsourcing. To our knowledge, this is the largest publicly available human labeled data set for open information extraction tasks. Using this labeled SAOKE data set, we train an end-to-end neural model using the sequence-to-sequence paradigm, called Logician, to transform sentences into facts. For each sentence, different to existing algorithms which generally focus on extracting each single fact without concerning other possible facts, Logician performs a global optimization over all possible involved facts, in which facts not only compete with each other to attract the attention of words, but also cooperate to share words. An experimental study on various types of open domain relation extraction tasks reveals the consistent superiority of Logician to other states-of-the-art algorithms. The experiments verify the reasonableness of SAOKE format, the valuableness of SAOKE data set, the effectiveness of the proposed Logician model, and the feasibility of the methodology to apply end-to-end learning paradigm on supervised data sets for the challenging tasks of open information extraction.

User experience is essential for human-computer dialogue systems. However, it is impractical to ask users to provide explicit feedbacks when the agents’ responses displease them. Therefore, in this paper, we explore to predict users’ imminent dissatisfactions caused by intelligent agents by analysing the existing utterances in the dialogue sessions. To our knowledge, this is the first work focusing on this task. Several possible factors that trigger negative emotions are modelled. A relation sequence model (RSM) is proposed to encode the sequence of appropriateness of current response with respect to the earlier utterances. The experimental results show that the proposed structure is effective in modelling emotional risk (possibility of negative feedback) than existing conversation modelling approaches. Besides, strategies of obtaining distance supervision data for pre-training are also discussed in this work. Balanced sampling with respect to the last response in the distance supervision data are shown to be reliable for data augmentation.

Writing academic articles in English is a challenging task for non-native speakers, as more effort has to be spent to enhance their language expressions. This paper presents an academic writing assistant system called Write-righter, which can provide real-time hint and recommendation by analyzing the input context. To achieve this goal, some novel strategies, e.g., semantic extension based sentence retrieval and LDA based sentence structure identification have been proposed. Write-righter is expected to help people express their ideas correctly by recommending top N most possible expressions.

Xin Wang, Yuanchao Liu, Chengjie Sun, Baoxun Wang, Xiaolong Wang. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.