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In his LREC 2004 invited talk when awarded by the first ever Antonio Zampolli prize for his essential contributions to the use of spoken and written language resources, Frederick Jelinek has used the title "Some of My Best Friends Are Linguists". He did so for many reasons, one of them being that he wanted to remove the perception that he dislikes linguists and linguistics after so many people used to cite his famous line from an old presentation at a Natural Language Processing Evaluation workshop in 1988, in which he said "Whenever I fire a linguist our system performance improves." [ABSTRACT FROM AUTHOR]

The MALACH project seeks to help users find information in a vast multilingual collections of untranscribed oral history interviews. This paper introduces the goals of the project and focuses on supporting access by users who are unfamiliar with the interview language. It begins with a review of the state of the art in crosslanguage speech retrieval; approaches that will be investigated in the project are then described. Czech was selected as the first non-English language to be supported, so results of an initial experiment with Czech/English cross-language retrieval are reported. [ABSTRACT FROM AUTHOR]

In this paper we describe the initial stages of the ASR component of the MALACH (Multilingual Access to Large Spoken Archives) project. This project will attempt to provide improved access to the large multilingual spoken archives collected by the Survivors of the Shoah Visual History Foundation (VHF) by advancing the state of the art in automated speech recognition. In order to train the ASR system, it is neccesary to manually transcribe a large amount of speech data, identify the appropriate vocabulary, and obtain relevant text for language modeling. We give a detailed description of the speech annotation process; show the specific properties of the spontaneous speech contained in the archives; and present a baseline speech recognition results. [ABSTRACT FROM AUTHOR]

In this two-for-one talk, first some difficult issues in morphology of inflective languages will be presented. Then, to lighten up this linguistically and computationally heavy issue, a half-forgotten history of statistical machine translation will be presented and contrasted with current state-of-the art (in a rather non-technical way). Computational morphology has been on and off the focus of computational linguistics. Only few of us probably remember the times when developing the proper formalisms has been in such a focus; a history poll might still find out that some people remember DATR-II, or other heavy-duty formalisms for dealing with the (virtually finite) world of words and their forms. Even unification formalisms have been called to duty (and the author himself admits to developing one). However, it is not the morphology itself (not even for inflective or agglutinative languages) that is causing the headache – with today΄s cheap space and power, simply listing all the thinkable forms in an appropriately hashed list is o.k. – but it΄s the disambiguation problem, which is apparently more difficult for such morphologically rich languages (perhaps surprisingly more for the inflective ones than agglutinative ones) than for the analytical ones. Since Ken Church΄s PARTS tagger, statistical methods of all sorts have been tried, and the accuracy of taggers for most languages is deemed pretty good today, even though not quite perfect yet. However, current results of machine translation are even farther from perfect (not just because of morphology, of course). The current revival of machine translation research will no doubt bring more progress. In the talk, I will try to remember the ″good old days″ of the original statistical machine translation system Candide, which was being developed at IBM Research since the late 80s, and show that as the patents then filed gradually fade and expire, there are several directions, tweaks and twists that have been used then but are largely ignored by the most advanced systems today (including, but not limited to morphology and tagging, noun phrase chunking, word sense disambiguation, named entity recognition, preferred form selection, etc.). I hope that not only this will bring some light to the early developments in the field of SMT and correct some misconceptions about the original IBM system often wrongly labeled as ″word-based″, but perhaps also inspire new developments in this area for the future – not only from the point of view of morphologically rich languages. [ABSTRACT FROM AUTHOR]