356 research outputs found
ANTIQUE: A Non-Factoid Question Answering Benchmark
Considering the widespread use of mobile and voice search, answer passage
retrieval for non-factoid questions plays a critical role in modern information
retrieval systems. Despite the importance of the task, the community still
feels the significant lack of large-scale non-factoid question answering
collections with real questions and comprehensive relevance judgments. In this
paper, we develop and release a collection of 2,626 open-domain non-factoid
questions from a diverse set of categories. The dataset, called ANTIQUE,
contains 34,011 manual relevance annotations. The questions were asked by real
users in a community question answering service, i.e., Yahoo! Answers.
Relevance judgments for all the answers to each question were collected through
crowdsourcing. To facilitate further research, we also include a brief analysis
of the data as well as baseline results on both classical and recently
developed neural IR models
Dublin City University at QA@CLEF 2008
We describe our participation in Multilingual Question Answering at CLEF 2008 using German and English as our source and target languages respectively. The system was built using UIMA (Unstructured Information Management Architecture) as underlying framework
Portable extraction of partially structured facts from the web
A novel fact extraction task is defined to fill a gap between current information retrieval and information extraction technologies. It is shown that it is possible to extract useful partially structured facts about different kinds of entities in a broad domain, i.e. all kinds of places depicted in tourist images. Importantly the approach does not rely on existing linguistic resources (gazetteers, taggers, parsers, etc.) and it ported easily and cheaply between two very different languages (English and Latvian). Previous fact extraction from the web has focused on the extraction of structured data, e.g. (Building-LocatedIn-Town). In contrast we extract richer and more interesting facts, such as a fact explaining why a building was built. Enough structure is maintained to facilitate subsequent processing of the information. For example, this partial structure enables straightforward template-based text generation. We report positive results for the correctness and interest of English and Latvian facts and for the utility of the extracted facts in enhancing image captions
Finding Answers to Definition Questions Using Web Knowledge Bases
PACLIC 23 / City University of Hong Kong / 3-5 December 200
NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics.
This thesis presents new methods that use natural language processing (NLP) driven models for summarizing research in scientific fields. Given a topic query in the form of a text string, we present methods for finding research articles relevant to the topic as well as summarization algorithms that use lexical and discourse information present in the text of these articles to generate coherent and readable extractive summaries of past research on the topic. In addition to summarizing prior research, good survey articles should also forecast future trends. With this motivation, we present work on forecasting future impact of scientific publications using NLP driven features.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113407/1/rahuljha_1.pd
Socratic Pretraining: Question-Driven Pretraining for Controllable Summarization
In long document controllable summarization, where labeled data is scarce,
pretrained models struggle to adapt to the task and effectively respond to user
queries. In this paper, we introduce Socratic pretraining, a question-driven,
unsupervised pretraining objective specifically designed to improve
controllability in summarization tasks. By training a model to generate and
answer relevant questions in a given context, Socratic pretraining enables the
model to more effectively adhere to user-provided queries and identify relevant
content to be summarized. We demonstrate the effectiveness of this approach
through extensive experimentation on two summarization domains, short stories
and dialogue, and multiple control strategies: keywords, questions, and factoid
QA pairs. Our pretraining method relies only on unlabeled documents and a
question generation system and outperforms pre-finetuning approaches that use
additional supervised data. Furthermore, our results show that Socratic
pretraining cuts task-specific labeled data requirements in half, is more
faithful to user-provided queries, and achieves state-of-the-art performance on
QMSum and SQuALITY.Comment: To appear at ACL 202
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