Guy Lapalme

The design and building of Enchère, a distributed electronic marketing system

Building and prototyping an agricultural electronic marketing system involved experimenting with distributed synchronization, atomic activity, and commit protocols and recovery algorithms.

Human interaction for high-quality machine translation

Introductionn Translation from a source language into a target language has become a very important activity in recent years, both in official institutions (such as the United Nations and the EU, or in the parliaments of multilingual countries like Canada and Spain), as well as in the private sector (for example, to translate users manuals or newspapers articles). Prestigious clients such as these cannot make do with approximate translations; for all kinds of reasons, ranging from the legal obligations to good marketing practice, they require target-language texts of the highest quality. The task of producing such high-quality translations is a demanding and time-consuming one that is generally conferred to expert human translators. The problem is that, with growing globalization, the demand for high-quality translation has been steadily increasing, to the point where there are just not enough qualified translators available today to satisfy it. This has dramatically raised the need for improved machine translation (MT) technologies.n The field of MT has undergone something of a revolution over the last 15 years, with the adoption of empirical, data-driven techniques originally inspired by the success of automatic speech recognition. Given the requisite corpora, it is now possible to develop new MT systems in a fraction of the time and with much less effort than was previously required under the formerly dominant rule-based paradigm. As for the quality of the translations produced by this new generation of MT systems, there has also been considerable progress; generally speaking, however, it remains well below that of human translation. No one would seriously consider directly using the output of even the best of these systems to translate a CV or a corporate Web site, for example, without submitting the machine translation to a careful human revision. As a result, those who require publication-quality translation are forced to make a diffcult choice between systems that are fully automatic but whose output must be attentively post-edited, and computer-assisted translation systems (or CAT tools for short) that allow for high quality but to the detriment of full automation.n Currently, the best known CAT tools are translation memory (TM) systems. These systems recycle sentences that have previously been translated, either within the current document or earlier in other documents. This is very useful for highly repetitive texts, but not of much help for the vast majority of texts composed of original materials.n Since TM systems were first introduced, very few other types of CAT tools have been forthcoming. Notable exceptions are the TransType system and its successor TransType2 (TT2). These systems represent a novel rework-ing of the old idea of interactive machine translation (IMT). Initial efforts on TransType are described in detail in Foster; suffice it to say here the systems principal novelty lies in the fact the human-machine interaction focuses on the drafting of the target text, rather than on the disambiguation of the source text, as in all former IMT systems.n In the TT2 project, this idea was further developed. A full-fledged MT engine was embedded in an interactive editing environment and used to generate suggested completions of each target sentence being translated. These completions may be accepted or amended by the translator; but once validated, they are exploited by the MT engine to produce further, hopefully improved suggestions. This is in marked contrast with traditional MT, where typically the system is first used to produce a complete draft translation of a source text, which is then post-edited (corrected) offline by a human translator. TT2s interactive approach offers a significant advantage over traditional post-editing. In the latter paradigm, there is no way for the system, which is off-line, to benefit from the users corrections; in TransType, just the opposite is true. As soon as the user begins to revise an incorrect segment, the system immediately responds to that new information by proposing an alternative completion to the target segment, which is compatible with the prefix that the user has input.n Another notable feature of the work described in this article is the importance accorded to a formal treatment of human-machine interaction, something that is seldom considered in the now-prevalent framework of statistical pattern recognition.

Supervised machine learning for summarizing legal documents

This paper presents a supervised machine learning approach for summarizing legal documents A commercial system for the analysis and summarization of legal documents provided us with a corpus of almost 4,000 text and extract pairs for our machine learning experiments That corpus was pre-processed to identify the selected source sentences in extracts from which we generated legal structured data We finally describe our sentence classification experiments relying on a Naive Bayes classifier using a set of surface, emphasis, and content features.

An automatic system for summarization and information extraction of legal information

This paper presents an information system for legal professionals that integrates natural language processing technologies such as text classification and summarization. We describe our experience in the use of a mix of linguistics aware transductor and XML technologies for bilingual information extraction from judgements in both French and English within a legal information and summarizing system. We present the context of the work, the main challenges and how they were tackled by clearly separating language and domain dependent terms and vocabularies. After having been developed on the immigration law domain, the system was easily ported to the intellectual property and tax law domains.

Machine Translation of Legal Information and Its Evaluation

This paper presents the machine translation system known as TransLI (Translation of Legal Information) developed by the authors for automatic translation of Canadian Court judgments from English to French and from French to English. Normally, a certified translation of a legal judgment takes several months to complete. The authors attempted to shorten this time significantly using a unique statistical machine translation system which has attracted the attention of the federal courts in Canada for its accuracy and speed. This paper also describes the results of a human evaluation of the output of the system in the context of a pilot project in collaboration with the federal courts of Canada.

Learning opinions in user-generated web content

The user-generated Web content has been intensively analyzed in Information Extraction and Natural Language Processing research. Web-posted reviews of consumer goods are studied to find customer opinions about the products. We hypothesize that nonemotionally charged descriptions can be applied to predict those opinions. The descriptions may include indicators of product size (tall), commonplace (some), frequency of happening (often), and reviewer certainty (maybe). We first construct patterns of how the descriptions are used in consumer-written texts and then represent individual reviews through these patterns. We propose a semantic hierarchy that organizes individual words into opinion types. We run machine learning algorithms on five data sets of user-written product reviews: four are used in classification experiments, another one for regression and classification. The obtained results support the use of non-emotional descriptions in opinion learning.

Enhancing the Bilingual Concordancer TransSearch with Word-Level Alignment

Despite the impressive amount of recent studies devoted to improving the state of the art of Machine Translation (MT), Computer Assisted Translation (CAT) tools remain the preferred solution of human translators when publication quality is of concern. In this paper, we present our perspectives on improving the commercial bilingual concordancer TransSearch , a Web-based service whose core technology mainly relies on sentence-level alignment. We report on experiments which show that it can greatly benefit from statistical word-level alignment.

Using Clustering to Personalize Visualization

The goal of our work is to propose models or methods to personalize the visualization of a large amount of weather information in a simple way and to make sure that a user can analyze all needed information. We personalize this visualization for each user according to an automatically detected profile based on clustering. Clustering is used to group users who are similar to current user and then set the visualization variables according to the visualizations of those users.

Narrative Generation from Extracted Associations

In [1], we study how causal relations may be used to improve narrative generation from real-life temporal data. We describe a method for extracting potential causal relations from temporal data and for structuring a generated report. The method is applied to the generation of reports highlighting unusual combinations of events in the Activity of Daily Living (ADL) domain. Our experiment applies association rules discovery techniques in [2] for selecting candidate associations based on three properties: frequency, confidence and significance. We assume that temporal proximity and temporal precedence are indicators of potential causality. The generation of a report from the ADL data for a given period follows a pipeline architecture. The first stage is data interpretation, which consists of finding instances of the previously selected association rules in the input. For each of those, one or more semantic relations are introduced as part of a hypothetic interpretation of the input data. Next those relations are used to plan the document as a whole in the document planning stage. The output is a rhetorical structure which is then pruned to keep only the most important events and relations. Follows a microplanning stage that plans the phrases and lexical units expressing the events and rhetorical relations. This produces a lexico-syntactic specification that is realised as natural language text in the last stage: surface realisation. After analysing the results, the extracted relations seem to be useful to locally link activities with explicit rhetorical relations. However, further work is needed to better exploit them for improving coherence at the global level.

JSREAL: A Text Realizer for Web Programming

The web is constantly growing and its documents, getting progressively more dynamic, are well-suited to presentation automation by a text realizer. Current browser-based information display systems have mostly focused on the display and layout of textual data, restricting the generation of nonnumerical informations to canned text or formatted strings. We describe JSreal, a French text realizer implemented in Javascript. It allows its user to build a variety of French expressions and sentences, combined with HTML tags to easily integrate them into web pages to produce dynamic output depending on the content of the page.