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Multilingual (or Cross-lingual) Information Retrieval
Editors: Judith Klavans and Eduard Hovy
Robert E. Frederking
Akitoshi Okumura, Kai Ishikawa, and Kenji Satoh
The term Multilingual Information Retrieval (MLIR) involves the study of systems that accept queries for information in various languages and return objects (text, and other media) of various languages, translated into the user's language. The rapid growth and online availability of information in many languages has made this a highly relevant field of research within the broad umbrella of language processing research. We ignore here issues pertaining to Machine Translation (Chapter 4) and Multimedia (Chapter 9), and focus on the extensions required of traditional Information Retrieval (IR) to handle more than one language.
2.1 Multilingual Information Retrieval
2.1.1 Definition and Terms
Multilingual Information Retrieval (MLIR) refers to the ability to process a query for information in any language, search a collection of objects, including text, images, sound files, etc., and return the most relevant objects, translated if necessary into the user's language. The explosion in recent years of freely-distributed unstructured information in all media, most notably on the World Wide Web, has opened the traditional field of Information Retrieval (IR) up to include image, video, speech, and other media, and has extended out to include access across multiple languages. Being new, MLIR will probably also include the historically excluded access mechanisms typical of libraries involving structured data, such as MARC catalogue records.
The general field of MLIR has expanded in several directions, focusing on different issues; what exactly is within its purview remains open to discussion. It is generally agreed, however, that Machine Translation proper (seeChapter 4) and Multimedia processing (see Chapter 9) are not included. Nonetheless, several new terms have arisen around the new IR, each with a slight variation in emphasis, inclusiveness, or historical association with related fields. For example, recent research in multilingual information retrieval, such as (Fluhr et al., 1998) in (Grefenstette, 1998), includes descriptive catalogue data from libraries as well as unstructured data. Hull and Grefenstette (1996) list five uses of the term MLIR:
In addition to MLIR, four related terms have been used:
1. Multilingual Information Access (MLIA). The broadest possible term to use is Multilingual Information Access, which refers to query, retrieval, and presentation of information in any language. The term MLIA is used in the NSF-EU working groups (Klavans and Schäuble, 1998). In general, the use of information access rather than retrieval implies a more general set of access functions, including those that have been part of the traditional library, as well as other modalities of access to other media. Access could refer to the use of speech input for video output, where the language component could consist of close-captioned text or text from speech recognition, or catalogue querying to metadata. The term information access came into use recently as a way to broaden the historically narrower use of information retrieval.
2. Multilingual Information Retrieval (MLIR). This term refers to the ability to process a query in any language and return objects, such as text, images, sound files, etc., relevant to the user query in any language. Historically, however, Information Retrieval (IR) as a field involved a group of researchers from the unstructured text data base community who employed statistical methods to match query and document (Salton, 1988). In general, this work was English dominated, given the amount of digital information made available to the research community in the early years in English, and excluded access mechanisms typical of libraries involving structured data, such as MARC catalogue records. Thus MLIR as used in this chapter denotes a significantly wider field of interest than that of traditional IR.
3. Cross-lingual Information Access. The use of the term cross-lingual refers (in this context) to bridging two languages, rather than the ability to access information in any language starting with input any language. Systems with cross-lingual capability can accept a query in language L1 or L2, for example English and French, and are capable of returning documents in either L1 or L2. (In other meetings, the term cross-lingual (or translingual) has been used to distinguish systems that cross a language barrier, as opposed to multiple monolingual systems as in TREC.) This term logically includes access via catalogue record and other structured indexing, as for MLIA.
4. Cross-lingual Information Retrieval (CLIR). CLIR
generally implies a relationship to IR, with all the implications that
apply to MLIR. At the 1997 Cross-language Information Retrieval Spring Symposium of the American Association of Artificial Intelligence (Oard et al., 1997), CLIR was defined with the following research challenge:
Given a query in any medium and any language, select relevant items from a multilingual multimedia collection which can be in any medium and any language, and present them in the style or order most likely to be useful to the user, with identical or near-identical objects in different media or languages appropriately identified. This definition of the requirements of a system gives full recognition to the query, retrieval, presentation requirements of a working system from a user perspective, and encapsulates succinctly the full set of capabilities to be included. However, its breadth makes it fit well with a definition of MLIA, the most general term, rather than CLIR, a more precise term.
2.1.2 MLIR: Linking and Hybridizing IR and MT
Multilingual Information Retrieval is a hybrid subject area, interacting with or encompassing several other fields. Section 2.5 discusses related fields.
How MLIR Relates to Information Retrieval
MLIR is an application of information retrieval. In many respects, as discussed above, the two fields share exactly the same goals; as such, well-known IR techniques such as vector space indexing, latent semantic indexing (LSI), similarity functions for matching documents, and query processing procedures are equally useful in MLIR. However MLIR differs from IR in several significant ways. Most important, IR involves no translation component, since only one language is involved. The related but not identical problems of translating queries and documents are discussed below. Subsidiary problems, such as keeping track of translations across several languages, are also not part of the standard monolingual information retrieval process.
How MLIR Relates to and Uses Machine Translation
The goal in machine translation (MT; seeChapter 4) is to convert a text, written in language L1, into a coherent and accurate translation in language L2. To do so, most MT systems convert the input text, usually sentence by sentence, into a series of progressively more abstract internal representations, in which sentence-internal relationships are determined and the intended meaning of each word is identified. Armed with this information, the appropriate conversions are made to support the output language, upon which output realization, usually also sentence by sentence, is performed. MT requires that the meaning of each individual word be known (as does accurate IR); without this knowledge, homographs (for example plane, which can refer to an airplane, carpentry tool, geometric surface, the action of skimming over water, and several other meanings) cannot be translated into their intended foreign words. Without word translation, no output is possible.
Can MLIR be Achieved by Coupling IR and MT?
Unfortunately, while at first blush it may seem that MLIR is simply a matter of coupling IR and MT engines, the special nature of MLIR places constraints on the input to MT that makes a straightforward coupling infeasible. At one extreme, some recent MLIR research has explored extending IR-based indexing techniques to directly bridge language gaps with no explicit translation step at all; see Sections 2.2.2 and 2.3.1 below. Arguments regarding the special nature of MLIR, contained in the NSF-EU MLIA Working Group White Paper (Klavans and Schäuble, 1998), are summarized here.
Differences between the two types of input submitted by MLIR for translation—queries and documents—necessitate two different types of Machine Translation. In the case of queries, the input to MT is a set of disconnected words, or possibly multi-word phrases. There is no call for MT to parse the input, since no syntactic sentence structure can be found. More seriously, the MT system cannot apply traditional methods of wordsense disambiguation, since the input is not a semantically coherent text. It will have to employ other (possibly IR-like) methods to determine the sense of each polysemous word in order to furnish accurate translations. On the other hand, there is no need to produce a linear, coherent output, and in fact multiple (correct) translations of a query term can provide a form of query expansion, which can improve IR performance. Finally, the processes of sentence planning and sentence realization are irrelevant when the input is a string of isolated query words. Without accurate queries, IR accuracy falls dramatically (results of recent studies are given later in this chapter).
For the stage of IR after retrieval (that is, in the case of retrieved documents), in contrast, documents can be translated back into the user's language using the normal methods of MT. However, also for this part of MLIR, partial translation, or keyword extraction and translation, is often adequate for the user's needs. In particular, given the computational expense of MT, it may be inefficient to translate a full document that the user later determines is not exactly what was desired. In addition, fully general purpose MT (especially between a wide variety of languages) is a very difficult problem. Translating a few keywords or a summary (seeChapter 3) is often a wise policy.
Several additional differences between monolingual IR and MLIR arise if the user is familiar with more than one language too. In particular, the user interface must provide differential display capabilities to reflect differing language proficiency levels of users. When more than one user receives the results, translation into several languages may have to be provided. Furthermore, depending on the user's level of sophistication, translation of different elements at different stages can be provided to users for a range of information access needs, including keyword translation, term translation, title translation, abstract translation, specific paragraph translation, caption translation, full document translation, etc. Finally, monolingual IR users can also take advantage of the results of MLIR. Simply the knowledge that a particular query will access a certain number of documents in other languages could, in itself, be valuable information, even if translations are not required.
Thus for MLIR much of the typical MT machinery is irrelevant, or at best only partially relevant. The differences with traditional MT mean that MLIR cannot simply employ MT engines as front-end query translators and back-end document translators.
Rather, efficient ways of coupling together the internal processes of IR and MT engines are required, allowing them to employ the results of the other's intermediate results. It is inevitable that second-generation MLIR systems will exhibit some more-than-surface integration of MT and IR modules.
2.1.3 Key Technical Issues for MLIR
We discuss three different positions on what are the key problems in MLIR. Grefenstette (1998) focuses on term choice and filtering. Oard (1998) presents user-centered challenges. Finally, Klavans (1999) outlines a two-part view that accommodates system-directed and user-directed research issues.
Grefenstette (1998) outlines three problems involving the processing of query terms for MLIR:
This problem requires knowing how terms map between languages. Since little or no contextual text is present in the query to help with term disambiguation, this involves knowing the full range of choices of translations, not just one possible translation, coupled with an understanding how different domains affect translation possibilities.
The second problem deals with determining how to filter, from all possible choices, which ones should be retained in the current application. Unlike MT, a MLIR system can retain a wider set of possibilities that can later be automatically filtered, depending on the kinds of variants that are permitted. Thus the MLIR system has to balance the amount of inaccurate translations (noise) that degrade results against the amount of processing performed to disambiguate the terms and ensure accuracy.
Given that it is advisable to retain a set of well-chosen possible terms for the best retrieval performance, a problem new to MLIR arises. The possibility of assigning alternate weights to different translations permits more accurate term choice. For example, in a compound term such as "morphological change", the first word is quite narrow in translation possibilities (e.g., in French, only one translation la morphologie) while the second is more general ("change" could be changement or monnaie). In such cases, more weight could be given to the first word's translation than to the second. This problem is compounded by the fact that some multi-word terms do not decompose, but should be treated as a collocation. Thus, mechanisms for weighing alternatives must consider individual word translation weights as well as multi-word term translation weights.
Grefenstette points out that the first two problems are also found in machine translation, and still require research for fully effective solutions. The third problem is one that clearly distinguishes MLIR from both MT and IR.
Oard (1998), in presentations during the Workshops on MLIR, outlined a historical view of CLIR that is user-centered in nature. He views the overall problem of CLIR as a series of processes, including query formulation and document selection, involving feedback from system to user and from user to system. The system-internal processes of indexing, document processing, and matching are treated as components supporting direct user interaction. He presents three points of historical perspective:
Oard's five challenges for the next five years are given in Section 2.4 below.
Klavans (1999) approaches the central problems in a somewhat different way, focusing on two sets of issues. One set involves three questions relating to the parts of the query-retrieval process, and the other set relates to user needs.
Usability Issues. IR systems present two main interface challenges: first, how to permit a user to input a query in a natural and intuitive way, and second, how to enable the user to interpret the returned results. A component of the latter encompasses ways to permit a user to comment and provide feedback on results and to iteratively improve and refine results. MLIR brings an added complexity to the standard IR task. Users can have different abilities for different languages, affecting their ability to form queries and interpret results. For example, a user might be proficient in understanding documents in French, but could not produce a query in French. In this case, the user will need to formulate a query in his native language, but will want documents returned only in French, not translated. At the same time, this user may have spotty knowledge of German. In this case, he might request a set of key terms translated to his native language, and not want to view source documents in German at all. Or he may simply want a numerical count, in order to know that for a given query, there are a certain number in German, a certain number in French, a certain number in Vietnamese, and so on. In addition, knowing the specific sources of relevant information may also be very valuable.
Since research and applications in MLIR are so new, a full understanding of user needs has yet to be developed and tested. However, these needs differ from simple MT needs, given the user query production and refinement stages.
2.1.4 Summary of Technical Challenges
MLIR involves at least the following four technical challenges:
2.2 Where We Were Five Years Ago
2.2.1 Capabilities Then
The lure of cross language information retrieval attracted experimentation by the IR community early on. Already in 1971, Salton showed that the use of a transfer dictionary for English and French (a bilingual wordlist with predefined mappings between terms) could be used to translate from a query in one language to another (Salton, 1971). This experiment, although ignoring the realistic and challenging problem of ambiguity, nonetheless served the information retrieval community well in providing a model for a viable approach to cross language IR. However, at the same time, the experiment also illustrated some of the exceedingly difficult problems in the language translation and mapping component of a system, namely one to many mappings, gaps in term translations, and ambiguity. Similarly, in a manual test with a small corpus, Pevzner (1972) showed for English and Russian that a controlled thesaurus can be used effectively for query term translation.
For nearly twenty years, the areas of IR and MT remained separate, leaving MLIR somewhat dormant. Apart from a few forays into refining these early techniques, all significant advances in MLIR have been made in the past five years. This is not surprising, given that increased amounts of information are becoming available in electronic format, and the economy is globalizing.
2.2.2 Major Methods, Techniques, and Approaches Five Years Ago
We discuss the problem within the framework outlined above.
System issues include the following.
Usability issues include the following. Early experiments were performed at such a small scale, more in the nature of proof-of-concept rather than full-fledged large-scale systems. User feedback and user needs were simply not part of what was tested.
2.2.3 Major Bottlenecks and Problems Five Years Ago
The three major bottlenecks of the early part of this decade still persist. They are: limited resources for building domain and language models; limited new technologies for coping with size of collections; and limited understanding of the myriad of user needs.
2.3 Where We Are Today
The burgeoning field of MLIR field is clearly in evidence, as can be seen in the bibliography in the first major review article on the topic (Oard and Dorr, 1996). Papers cited include related work on machine translation, including some research translated from Russian. There are 16 citations prior to 1980, 10 from 1980-89, and 52 from 1990 to early 1996. The first major book to be published on the topic (Grefenstette, 1998) reflects the same temporal bias. This work is slanted towards IR rather than toward MT. It contains 11 citations prior to 1980, 25 from 1980-89, and 101 from 1990 to very early 1998.
2.3.1 Major Methods, Techniques, and Approaches Now
Following the format above, we divide the methods into system-centered and user-centered concerns, although each provides feedback to the other.
System issues include the following:
Usability issues include the following. The development of effective MLIR technology will have no impact if the user's needs and operation patterns are not considered. Since MLIR is a growing field, and since applications are just emerging, formative studies of usability are essential. Currently, there are a limited number of systems in early operation which are providing important data (e.g., EuroSpider, the translate function of AltaVista, multilingual catalogue access). The incorporation of users in the relevance feedback loop is particularly important, since user needs vary greatly. A full review of user needs is found in (Klavans and Schäuble, 1998).
2.3.2 Major Bottlenecks and Problems
Since this is a new field, the bottlenecks listed in Section 2.2.3, evident in earlier years, persist.
2.4 Where We Will Be in Five Years
The growing amount of multilingual corpora is providing a valuable and as yet untapped resource for MLIR. Such corpora are essential to building successful dynamic term and phrase translation thesauri, which is, in turn, key to effective indexing and matching. One of the key challenges is in devising efficient yet linguistically informed methods of tapping these resources, methods which combine the best of what is know about fast statistical techniques along with more knowledge based symbolic methods. Even promising new techniques, such as translingual LSI (Landauer et al., 1998) and related techniques (Carbonell et al., 1997), will most probably still rely on parallel corpora. Such corpora are often difficult to find, and very expensive to prepare. This has been the motivation for the work on comparable corpora. However, more and more are being created electronically, especially to conform to legal requirements for the European Union. The issues surrounding corpora are extensively discussed inChapter 1.
An important class of techniques involves machine learning, as applied to the cross-language term mapping problem. Since term translation, loosely defined, is at the core of query processing, document processing, and matching, it is an important process to do thoroughly and accurately. Even if multiple translations are retained in the MLIR process, obtaining a sensible set of domain linked terms is an important and central task. One way to obtain these term dictionaries is through parallel corpora, but statistical processing is typically difficult to fine tune. As discussed inChapter 6, machine learning techniques are a fundamental enhancement of the power of language processing systems and hold particular promise in this area as well.
Finally, it is to be hoped that our understanding of user needs and user interactions with MLIR systems will be significantly better in five years than it is now. As early systems emerge and are tested in the field, a range of flexible and fluid applications that can learn and dynamically adjust to the users' levels of competence, across languages and across domains, should appear. One possible example of this type of flexible application might be human-aided MT systems for producing gisting-quality translations of retrieved documents, which would allow the user to make a personal time/quality tradeoff: the longer the user interacted with the translator, the better the resulting output. Most probably, these systems will incorporate multimedia seamlessly and permit multimodal input and output. Such capabilities will provide maximum usability.
2.4.1 Expected Capabilities
Oard (1998) outlines five challenges for the next five years:
2.4.2 Expected Bottlenecks in Five Years
Four key issues must be overcome in order to achieve effective MLIR. Some of these issues also apply to IR and MT independently.
2.5 Juxtaposition of This Area with Other Areas
Two major classes of technical issues must be addressed when dealing with multilingual data:
First, technical issues involving data exchange, with a set of attendant sub-issues. This includes questions such as character encoding, font displays, browser/display issues, etc. Such issues have implications for metadata for the Internet, international sharing of bibliographic records, and transliteration and transcription systems.
Second, natural language questions, also with a set of attendant research issues. This includes natural language processing technologies (e.g., syntactic or semantic analysis), machine translation, information retrieval (or information discovery) in multiple languages, speech processing, and summarization. Also included are questions of multilingual language resources, such as dictionaries and thesauri, corpora, and test collections.
The new application of MLIR draws on achievements and techniques in several related areas. However, the challenges unique to MLIR must be handled independently. Listing some of the relevant technologies, these include:
Several potentially valuable connections have not yet been made. The Database and Computational Linguistics research and development communities, for example, contain in their members a great deal of relevant expertise. The National Science Foundation PI meeting on Information and Data Management (1998) concluded that closer links between the IR and Database communities would be beneficial to each. Similarly, the human-computer interaction / multimedia community offers important insights into ensuring user-driven design of systems.
In order to facilitate cross-fertilization, a series of small workshops to define new projects, and a series of very small seed projects, would help the specification of prototype systems and the elucidation of complex problem areas. Projects should be interdisciplinary, very limited in scope, with well-defined goals leaving room for exploratory research. The results of such cross-fertilization would depend on the backgrounds of the potential participants. Assembling a group from commerce to assist computer scientists in specifying the needs that MLIR systems must address, or focus groups from high information-needs communities, such as journalism and finance, could be used to specify new projects and prototypes and guide the direction of research in beneficial directions.
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