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Machine translation (MT) systems do not currently achieve optimal quality translation on free text, whatever translation method they employ. Our hypothesis is that the quality of MT will improve if an MT environment uses output from a variety of MT systems working on the same text. In the latest version of the Pangloss MT project, we collect the results of three translation engines --typically, subsentential chunks --in a chart data structure. Since the individual MT systems operate completely independently, their results may be incomplete, conflicting, or redundant. We use simple scoring heuristics to estimate the quality of each chunk, and find the highest-score sequence of chunks (the "best cover"). This paper describes in detail the combining method, presenting the algorithm and illustrations of its progress on one of many actual translations it has produced. It uses dynamic programming to efficiently compare weighted averages of sets of adjacent scored component translations. The current system operates primarily in a human-aided MT mode. The translation delivery system and its associated post-editing aide are briefly described, as is an initial evaluation of the usefulness of this method. Individual M T engines will be reported separately and are not, therefore, described in detail here.

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This paper presents a semiautomatic technique for developing broad-coverage finite-state morphological analyzers for use in natural language processing applications. It consists of three components—elicitation of linguistic information from humans, a machine learning bootstrapping scheme, and a testing environment. The three components are applied iteratively until a threshold of output quality is attained. The initial application of this technique is for the morphology of low-density languages in the context of the Expedition project at NMSU Computing Research Laboratory. This elicit-build-test technique compiles lexical and inØectional information elicited from a human into a finite-state transducer lexicon and combines this with a sequence of morphographemic rewrite rules that is induced using transformation-based learning from the elicited examples. The resulting morphological analyzer is then tested against a test set, and any corrections are fed back into the learning procedure, which then builds an improved analyzer.

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A~M~'g~ef~ This t~i~ dc:~cdbcs a proeedmc Ru' lexical se.. le=,:![~ia~ of OlW, n-claSS k:xicN items in a natmal langp.age gca~:~atkm sysRm. A,l optimum R;xical seleetiol; ua}t~nig ~mlst [i~.: able tO Ira|nice. rt~.!lizati,'m decisions mldc; vary-. i~g rcugextna~ cit'cmastances. First, it must be N)le to iy;;,?-,i~ ~, withont the illlhlCUCe of coiitext~ based ou |llCT, ll. lug cm~e~poadcuces I~elwcen elemeuls of couccptual ill" imt a,,d ~hu k:xic.~l i~e~'emo~y of thc talget language. S(m-O~ld, it must t~.~ ~,bh~ to use contextl~d constlaims, as sup° F;~f~ by coll~,.:adoaM iaf(~nuatio~ ii~ tfie gcneralion lexb c(m. '~ hi~d, ihcru lilllSt ])!) illt oplion O~ realizing input tepresenh liolts laonomiaally or th~ongh detinite dc.scripliuns. t,'il!;iii3, the~e nntst alsu b(3 au option of tLsing elliplical co~tstruciions. The ~tttum of 1)ackgtouud kuowledgc and th{; all o~ithu~ v/c sugge.~t lbr this task are. dcscrilav& The icxical xetcctiou prt~dttre iS a part of a couq)fehgnsive j~eltcra{ton Systeub D[OGI,2qE%

Núñez et al.'s (2019) negative assessment of the field of cognitive science derives from evaluation criteria that fail to reflect the true nature of the field. In reality, the field is thriving on both the research and educational fronts, and it shows great promise for the future.