Humans use language to accomplish a wide variety of tasks - asking for and giving advice being one of them. In online advice forums, advice is mixed in with non-advice, like emotional support, and is sometimes stated explicitly, sometimes implicitly. Understanding the language of advice would equip systems with a better grasp of language pragmatics; practically, the ability to identify advice would drastically increase the efficiency of advice-seeking online, as well as advice-giving in natural language generation systems.
We present a dataset in English from two Reddit advice forums - r/AskParents and r/needadvice - annotated for whether sentences in posts contain advice or not. Our analysis reveals rich linguistic phenomena in advice discourse. We present preliminary models showing that while pre-trained language models are able to capture advice better than rule-based systems, advice identification is challenging, and we identify directions for future research.
Comments: To be presented at EMNLP 2020.
An increasing number of people now rely on online platforms to meet their health information needs. Thus identifying inconsistent or conflicting textual health information has become a safety-critical task. Health advice data poses a unique challenge where information that is accurate in the context of one diagnosis can be conflicting in the context of another. For example, people suffering from diabetes and hypertension often receive conflicting health advice on diet. This motivates the need for technologies which can provide contextualized, user-specific health advice. A crucial step towards…
ELLA: Empowering LLMs for Interpretable, Accurate and Informative Legal Advice
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；School of Electronics Engineering and Computer Science, Peking University（北京大学电子工程与计算机科学学院）
Over the last few years, there has been growing interest in learning models for physically grounded language understanding tasks, such as the popular blocks world domain. These works typically view this problem as a single-step process, in which a human operator gives an instruction and an automated agent is evaluated on its ability to execute it. In this paper we take the first step towards increasing the bandwidth of this interaction, and suggest a protocol for including advice, high-level observations about the task, which can help constrain the agent's prediction. We evaluate our approach …
Clinical knowledge in LLMs does not translate to human interactions
Oxford Internet Institute（牛津互联网研究所）；University of Oxford（牛津大学）；Nuffield Department of Primary Care（初级保健部门）；North Wales Medical School（北威尔士医学院）；Bangor University（班戈大学）；Contextual AI（上下文AI）；MLCommons；Factored AI；Birmingham Women’s and Children’s NHS Foundation Trust（伯明翰妇女和儿童国家卫生信托基金会）；Institute of Biomedical Engineering（生物医学工程研究所）
Paul G. Allen School of Computer Science & Engineering, University of Washington（华盛顿大学保罗·G·艾伦计算机科学与工程学院）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）
We propose TuringAdvice, a new challenge task and dataset for language understanding models. Given a written situation that a real person is currently facing, a model must generate helpful advice in natural language. Our evaluation framework tests a fundamental aspect of human language understanding: our ability to use language to resolve open-ended situations by communicating with each other. Empirical results show that today's models struggle at TuringAdvice, even multibillion parameter models finetuned on 600k in-domain training examples. The best model, a finetuned T5, writes advice that i…
The CL-SciSumm 2016 shared task introduced an interesting problem: given a document D and a piece of text that cites D, how do we identify the text spans of D being referenced by the piece of text? The shared task provided the first annotated dataset for studying this problem. We present an analysis of our continued work in improving our system's performance on this task. We demonstrate how topic models and word embeddings can be used to surpass the previously best performing system.
RIKEN（理化学研究所）；Ochanomizu University（御茶水女子大学）；Tohoku University（东北大学）；University of Groningen（格罗宁根大学）
Large crowdsourced datasets are widely used for training and evaluating neural models on natural language inference (NLI). Despite these efforts, neural models have a hard time capturing logical inferences, including those licensed by phrase replacements, so-called monotonicity reasoning. Since no large dataset has been developed for monotonicity reasoning, it is still unclear whether the main obstacle is the size of datasets or the model architectures themselves. To investigate this issue, we introduce a new dataset, called HELP, for handling entailments with lexical and logical phenomena. We…
Near-Zero-Shot Suggestion Mining with a Little Help from WordNet
Steklov Mathematical Institute at St. Petersburg（圣彼得堡斯捷克洛夫数学研究所）；St. Petersburg University（圣彼得堡大学）；Kazan (Volga Region) Federal University（喀山（伏尔加地区）联邦大学）；HSE University（高等经济大学）；Sber AI；Samsung Research（三星研究院）
In recent years online shopping has gained momentum and became an important venue for customers wishing to save time and simplify their shopping process. A key advantage of shopping online is the ability to read what other customers are saying about products of interest. In this work, we aim to maintain this advantage in situations where extreme brevity is needed, for example, when shopping by voice. We suggest a novel task of extracting a single representative helpful sentence from a set of reviews for a given product. The selected sentence should meet two conditions: first, it should be help…
Copenhagen Business School（哥本哈根商学院）；Metropolitan State University of Denver（丹佛大都会州立大学）
Classification of social media posts in emergency response is an important practical problem: accurate classification can help automate processing of such messages and help other responders and the public react to emergencies in a timely fashion. This research focused on classifying Facebook messages of US police departments. Randomly selected 5,000 messages were used to train classifiers that distinguished between four categories of messages: emergency preparedness, response and recovery, as well as general engagement messages. Features were represented with bag-of-words and word2vec, and mod…
In this paper, we present a method for identifying discourse marker usage in spontaneous speech based on machine learning. Discourse markers are denoted by special POS tags, and thus the process of POS tagging can be used to identify discourse markers. By incorporating POS tagging into language modeling, discourse markers can be identified during speech recognition, in which the timeliness of the information can be used to help predict the following words. We contrast this approach with an alternative machine learning approach proposed by Litman (1996). This paper also argues that discourse ma…
Lab-STICC/ENIB（STICC实验室/布雷斯特国立高等工程师学院）；Flinders University of South Australia（南澳弗林德斯大学）；CNRS（法国国家科学研究中心）
Human-Robot Interaction (HRI) is an emerging subfield of service robotics. While most existing approaches rely on explicit signals (i.e. voice, gesture) to engage, current literature is lacking solutions to address implicit user needs. In this paper, we present an architecture to (a) detect user implicit need of help and (b) generate a set of assistive actions without prior learning. Task (a) will be performed using state-of-the-art solutions for Scene Graph Generation coupled to the use of commonsense knowledge; whereas, task (b) will be performed using additional commonsense knowledge as wel…
Identifying Informational Sources in News Articles
Information Sciences Institute, University of Southern California（南加州大学信息科学研究所）；University of California, Los Angeles（加州大学洛杉矶分校）；Annenberg School of Communication, University of Southern California（南加州大学安纳伯格传播学院）
