2,493 research outputs found
Multilingual Schema Matching for Wikipedia Infoboxes
Recent research has taken advantage of Wikipedia's multilingualism as a
resource for cross-language information retrieval and machine translation, as
well as proposed techniques for enriching its cross-language structure. The
availability of documents in multiple languages also opens up new opportunities
for querying structured Wikipedia content, and in particular, to enable answers
that straddle different languages. As a step towards supporting such queries,
in this paper, we propose a method for identifying mappings between attributes
from infoboxes that come from pages in different languages. Our approach finds
mappings in a completely automated fashion. Because it does not require
training data, it is scalable: not only can it be used to find mappings between
many language pairs, but it is also effective for languages that are
under-represented and lack sufficient training samples. Another important
benefit of our approach is that it does not depend on syntactic similarity
between attribute names, and thus, it can be applied to language pairs that
have distinct morphologies. We have performed an extensive experimental
evaluation using a corpus consisting of pages in Portuguese, Vietnamese, and
English. The results show that not only does our approach obtain high precision
and recall, but it also outperforms state-of-the-art techniques. We also
present a case study which demonstrates that the multilingual mappings we
derive lead to substantial improvements in answer quality and coverage for
structured queries over Wikipedia content.Comment: VLDB201
MIRACLE Retrieval Experiments with East Asian Languages
This paper describes the participation of MIRACLE in NTCIR 2005 CLIR task. Although our group has a strong background and long expertise in Computational Linguistics and Information Retrieval applied to European languages and using Latin and Cyrillic alphabets, this was our first attempt on East Asian languages. Our main goal was to study the particularities and distinctive characteristics of Japanese, Chinese and Korean, specially focusing on the similarities and differences with European languages, and carry out research on CLIR tasks which include those languages. The basic idea behind our participation in NTCIR is to test if the same familiar linguisticbased techniques may also applicable to East Asian languages, and study the necessary adaptations
Teaching a New Dog Old Tricks: Resurrecting Multilingual Retrieval Using Zero-shot Learning
While billions of non-English speaking users rely on search engines every
day, the problem of ad-hoc information retrieval is rarely studied for
non-English languages. This is primarily due to a lack of data set that are
suitable to train ranking algorithms. In this paper, we tackle the lack of data
by leveraging pre-trained multilingual language models to transfer a retrieval
system trained on English collections to non-English queries and documents. Our
model is evaluated in a zero-shot setting, meaning that we use them to predict
relevance scores for query-document pairs in languages never seen during
training. Our results show that the proposed approach can significantly
outperform unsupervised retrieval techniques for Arabic, Chinese Mandarin, and
Spanish. We also show that augmenting the English training collection with some
examples from the target language can sometimes improve performance.Comment: ECIR 2020 (short
Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation
The Differentiable Search Index (DSI) is an emerging paradigm for information
retrieval. Unlike traditional retrieval architectures where index and retrieval
are two different and separate components, DSI uses a single transformer model
to perform both indexing and retrieval.
In this paper, we identify and tackle an important issue of current DSI
models: the data distribution mismatch that occurs between the DSI indexing and
retrieval processes. Specifically, we argue that, at indexing, current DSI
methods learn to build connections between the text of long documents and the
identifier of the documents, but then retrieval of document identifiers is
based on queries that are commonly much shorter than the indexed documents.
This problem is further exacerbated when using DSI for cross-lingual retrieval,
where document text and query text are in different languages.
To address this fundamental problem of current DSI models, we propose a
simple yet effective indexing framework for DSI, called DSI-QG. When indexing,
DSI-QG represents documents with a number of potentially relevant queries
generated by a query generation model and re-ranked and filtered by a
cross-encoder ranker. The presence of these queries at indexing allows the DSI
models to connect a document identifier to a set of queries, hence mitigating
data distribution mismatches present between the indexing and the retrieval
phases. Empirical results on popular mono-lingual and cross-lingual passage
retrieval datasets show that DSI-QG significantly outperforms the original DSI
model.Comment: 11 page
Cross-Lingual Adaptation using Structural Correspondence Learning
Cross-lingual adaptation, a special case of domain adaptation, refers to the
transfer of classification knowledge between two languages. In this article we
describe an extension of Structural Correspondence Learning (SCL), a recently
proposed algorithm for domain adaptation, for cross-lingual adaptation. The
proposed method uses unlabeled documents from both languages, along with a word
translation oracle, to induce cross-lingual feature correspondences. From these
correspondences a cross-lingual representation is created that enables the
transfer of classification knowledge from the source to the target language.
The main advantages of this approach over other approaches are its resource
efficiency and task specificity.
We conduct experiments in the area of cross-language topic and sentiment
classification involving English as source language and German, French, and
Japanese as target languages. The results show a significant improvement of the
proposed method over a machine translation baseline, reducing the relative
error due to cross-lingual adaptation by an average of 30% (topic
classification) and 59% (sentiment classification). We further report on
empirical analyses that reveal insights into the use of unlabeled data, the
sensitivity with respect to important hyperparameters, and the nature of the
induced cross-lingual correspondences
iCLEF 2006 Overview: Searching the Flickr WWW photo-sharing repository
This paper summarizes the task design for iCLEF 2006 (the CLEF interactive track).
Compared to previous years, we have proposed a radically new task: searching images
in a naturally multilingual database, Flickr, which has millions of photographs shared
by people all over the planet, tagged and described in a wide variety of languages.
Participants are expected to build a multilingual search front-end to Flickr (using
Flickr’s search API) and study the behaviour of the users for a given set of searching
tasks. The emphasis is put on studying the process, rather than evaluating its outcome
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