Translation-based Language Model Research Articles

Improving question retrieval in community question answering service using dependency relations and question classification

To build an effective community question answering (cQA) service, determining ways to obtain questions similar to an input query question is a significant research issue. The major challenges for question retrieval in cQA are related to solving the lexical gap problem and estimating the relevance between questions. In this study, we first solve the lexical gap problem using a translation‐based language model (TRLM). Thereafter, we determine features and methods that are competent for estimating the relevance between two questions. For this purpose, we explore ways to use the results of a dependency parser and question classification for category information. Head‐dependent pairs are first extracted as bigram features, called dependency bigrams, from the analysis results of the dependency parser. The probability of each category is estimated using the softmax approach based on the scores of the classification results. Subsequently, we propose two retrieval models—the dependency‐based model (DM) and category‐based model (CM)—and they are applied to the previous model, TRLM. The experimental results demonstrate that the proposed methods significantly improve the performance of question retrieval in cQA services.

Learning to suggest questions in social media

Social media systems with Q&A functionalities have accumulated large archives of questions and answers. Two representative types are online forums and community-based Q&A services. To enable users to explore the large number of questions and answers in social media systems effectively, it is essential to suggest interesting items to an active user. In this article, we address the problem of question suggestion, which targets at suggesting questions that are semantically related to a queried question. Existing bag-of-words approaches suffer from the shortcoming that they could not bridge the lexical chasm between semantically related questions. Therefore, we present a new framework, and propose the topic-enhanced translation-based language model (TopicTRLM), which fuses both the lexical and latent semantic knowledge. This fusing enables TopicTRLM to find semantically related questions to a given question even when there is little word overlap. Moreover, to incorporate the answer information into the model to make the model more complete, we also propose the topic-enhanced translation-based language model with answer ensemble. Extensive experiments have been conducted with real-world datasets. Experimental results indicate our approach is very effective and outperforms other popular methods in several metrics.

어휘 번역확률과 질의개념연관도를 반영한 검색 모델

One of the major challenge for retrieval performance is the word mismatch between user`s queries and documents in information retrieval. To solve the word mismatch problem, we propose a retrieval model based on the degree of association of query concept and word translation probabilities in translation-based model. The word translation probabilities are calculated based on the set of a sentence and its succeeding sentence pair. To validate the proposed method, we experimented on TREC AP test collection. The experimental results show that the proposed model achieved significant improvement over the language model and outperformed translation-based language model.

Learning to Suggest Questions in Online Forums

Online forums contain interactive and semantically related discussions on various questions. Extracted question-answer archive is invaluable knowledge, which can be used to improve Question Answering services. In this paper, we address the problem of Question Suggestion, which targets at suggesting questions that are semantically related to a queried question. Existing bag-of-words approaches suffer from the shortcoming that they could not bridge the lexical chasm between semantically related questions. Therefore, we present a new framework to suggest questions, and propose the Topicenhanced Translation-based Language Model (TopicTRLM) which fuses both the lexical and latent semantic knowledge. Extensive experiments have been conducted with a large real world data set. Experimental results indicate our approach is very effective and outperforms other popular methods in several metrics.

질문대답 아카이브에서 어휘 연관성을 이용한 질문 분류

Word mismatch is the most significant problem that causes low performance in question classification, whose questions consist of only two or three words that expressed in many different ways. So, it is necessary to apply word association in question classification. In this paper, we propose question classification method using translation-based language model, which use word translation probabilities for question-question pair that is learned in the same category. In the experiment, we prove that translation probabilities of question-question pairs in the same category is more effective than question-answer pairs in total collection.