Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

Task-Oriented Query Reformulation with Reinforcement Learning

Search engines play an important role in our everyday lives by assisting us in finding the information we need. When we input a complex query, however, results are often far from satisfactory. In this work, we introduce a query reformulation system based on a neural network that rewrites a query to maximize the number of relevant documents returned. We train this neural network with reinforcement learning. The actions correspond to selecting terms to build a reformulated query, and the reward is the document recall. We evaluate our approach on three datasets against strong baselines and show a relative improvement of 5-20% in terms of recall. Furthermore, we present a simple method to estimate a conservative upper-bound performance of a model in a particular environment and verify that there is still large room for improvements.Comment: EMNLP 201

Embedding Web-based Statistical Translation Models in Cross-Language Information Retrieval

Although more and more language pairs are covered by machine translation services, there are still many pairs that lack translation resources. Cross-language information retrieval (CLIR) is an application which needs translation functionality of a relatively low level of sophistication since current models for information retrieval (IR) are still based on a bag-of-words. The Web provides a vast resource for the automatic construction of parallel corpora which can be used to train statistical translation models automatically. The resulting translation models can be embedded in several ways in a retrieval model. In this paper, we will investigate the problem of automatically mining parallel texts from the Web and different ways of integrating the translation models within the retrieval process. Our experiments on standard test collections for CLIR show that the Web-based translation models can surpass commercial MT systems in CLIR tasks. These results open the perspective of constructing a fully automatic query translation device for CLIR at a very low cost.Comment: 37 page

Evaluating the implicit feedback models for adaptive video retrieval

Interactive video retrieval systems are becoming popular. On the one hand, these systems try to reduce the effect of the semantic gap, an issue currently being addressed by the multimedia retrieval community. On the other hand, such systems enhance the quality of information seeking for the user by supporting query formulation and reformulation. Interactive systems are very popular in the textual retrieval domain. However, they are relatively unexplored in the case of multimedia retrieval. The main problem in the development of interactive retrieval systems is the evaluation cost.The traditional evaluation methodology, as used in the information retrieval domain, is not applicable. An alternative is to use a user-centred evaluation methodology. However, such schemes are expensive in terms of effort, cost and are not scalable. This problem gets exacerbated by the use of implicit indicators, which are useful and increasingly used in predicting user intentions. In this paper, we explore the effectiveness of a number of interfaces and feedback mechanisms and compare their relative performance using a simulated evaluation methodology. The results show the relatively better performance of a search interface with the combination of explicit and implicit features

Japanese/English Cross-Language Information Retrieval: Exploration of Query Translation and Transliteration

Cross-language information retrieval (CLIR), where queries and documents are in different languages, has of late become one of the major topics within the information retrieval community. This paper proposes a Japanese/English CLIR system, where we combine a query translation and retrieval modules. We currently target the retrieval of technical documents, and therefore the performance of our system is highly dependent on the quality of the translation of technical terms. However, the technical term translation is still problematic in that technical terms are often compound words, and thus new terms are progressively created by combining existing base words. In addition, Japanese often represents loanwords based on its special phonogram. Consequently, existing dictionaries find it difficult to achieve sufficient coverage. To counter the first problem, we produce a Japanese/English dictionary for base words, and translate compound words on a word-by-word basis. We also use a probabilistic method to resolve translation ambiguity. For the second problem, we use a transliteration method, which corresponds words unlisted in the base word dictionary to their phonetic equivalents in the target language. We evaluate our system using a test collection for CLIR, and show that both the compound word translation and transliteration methods improve the system performance