Solving Open-Domain Multiple Choice Questions with Textual Entailment and Text Similarity Measures
نویسندگان
چکیده
In this paper, we present a system for automatically answering opendomain, multiple choice reading comprehension questions about short English narrative texts. The system is based on state-of-the-art text similarity measures, textual entailment metrics and coreference resolution and does not make use of any additional domain specific background knowledge. Each answer option is scored with a combination of all evaluation metrics and ranked according to their overall score in order to determine the most likely correct answer. Our best configuration achieved the second highest score across all competing system in the entrance exam grading challenge with a c@1 score of 0.375.
منابع مشابه
ECNU: One Stone Two Birds: Ensemble of Heterogenous Measures for Semantic Relatedness and Textual Entailment
This paper presents our approach to semantic relatedness and textual entailment subtasks organized as task 1 in SemEval 2014. Specifically, we address two questions: (1) Can we solve these two subtasks together? (2) Are features proposed for textual entailment task still effective for semantic relatedness task? To address them, we extracted seven types of features including text difference meas...
متن کاملUKP-BIU: Similarity and Entailment Metrics for Student Response Analysis
Our system combines text similarity measures with a textual entailment system. In the main task, we focused on the influence of lexicalized versus unlexicalized features, and how they affect performance on unseen questions and domains. We also participated in the pilot partial entailment task, where our system significantly outperforms a strong baseline.
متن کاملTextual Entailment as a Directional Relation
This paper presents three methods for solving the problem of textual entailment, obtained from an equal number of text-to-text similarity metrics. The first method starts with the directional measure of text-to-text similarity presented in Corley and Mihalcea (2005), and integrates word sense disambiguation and several heuristics. The second method exploits the relations between the cosine dire...
متن کاملMCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text
We present MCTest, a freely available set of stories and associated questions intended for research on the machine comprehension of text. Previous work on machine comprehension (e.g., semantic modeling) has made great strides, but primarily focuses either on limited-domain datasets, or on solving a more restricted goal (e.g., open-domain relation extraction). In contrast, MCTest requires machin...
متن کاملECNUCS: Recognizing Cross-lingual Textual Entailment Using Multiple Text Similarity and Text Difference Measures
This paper presents our approach used for cross-lingual textual entailment task (task 8) organized within SemEval 2013. Crosslingual textual entailment (CLTE) tries to detect the entailment relationship between two text fragments in different languages. We solved this problem in three steps. Firstly, we use a off-the-shelf machine translation (MT) tool to convert the two input texts into the sa...
متن کامل