Left language model state for syntactic machine translation

Many syntactic machine translation decoders, including Moses, cdec, and Joshua, implement bottom-up dynamic programming to integrate N-gram language model proba-bilities into hypothesis scoring. These decoders concatenate hypotheses according to grammar rules, yielding larger hy-potheses and eventually complete translations. When hy-potheses are concatenated, the language model score is ad-justed to account for boundary-crossing n-grams. Words on the boundary of each hypothesis are encoded in state, con-sisting of left state (the first few words) and right state (the last few words). We speed concatenation by encoding left state using data structure pointers in lieu of vocabulary in-dices and by avoiding unnecessary queries. To increase the decoder’s opportunities to recombine hypothesis, we mini-mize the number of words encoded by left state. This has the effect of reducing search errors made by the decoder. The resulting gain in model score is smaller than for right state minimization, which we explain by observing a relationship between state minimization and language model probability. With a fixed cube pruning pop limit, we show a 3-6 % re-duction in CPU time and improved model scores. Reducing the pop limit to the point where model scores tie the baseline yields a net 11 % reduction in CPU time. 1

Screening for Microtubule-Disrupting Antifungal Agents by Using a Mitotic-Arrest Mutant of Aspergillus nidulans and Novel Action of Phenylalanine Derivatives Accompanying Tubulin Loss

The microtubule, which is one of the major targets of anthelmintics, anticancer drugs, and fungicides, is composed mainly of α- and β-tubulins. We focused on a unique characteristic of an Aspergillus nidulans benA33 mutant to screen for microtubule-disrupting antifungal agents. This mutant, which has a β-tubulin with a mutation of a single amino acid, undergoes mitotic arrest due to the formation of hyperstable microtubules at 37°C. The heat sensitivity of the mutant is remedied by some antimicrotubule agents. We found that an agar plate assay with the mutant was able to distinguish three types of microtubule inhibitors. The growth recovery zones of the mutant were formed around paper disks containing microtubule inhibitors, including four benzimidazoles, ansamitocin P-3, griseofulvin, and rhizoxin, on the agar plate at 37°C. Nocodazole, thiabendazole, and griseofulvin reversed the mitotic arrest of the mutant and promoted its hyphal growth. Ansamitocin P-3 and rhizoxin showed growth recovery zones around the growth-inhibitory zones. Benomyl and carbendazim also reversed mitotic arrest but produced weaker growth recovery than the aforementioned drugs. Other microtubule inhibitors, such as colchicine, Colcemid, paclitaxel, podophyllotoxin, TN-16, vinblastine, and vincristine, as well as some cytoskeletal inhibitors tested, did not show such activity. In our screening, we newly identified two mycotoxins, citrinin and patulin, two sesquiterpene dialdehydes, polygodial and warburganal, and four phenylalanine derivatives, arphamenine A, l-2,5-dihydrophenylalanine (DHPA), N-tosyl-l-phenylalanine chloromethylketone, and N-carbobenzoxy-l-phenylalanine chloromethyl ketone. In a wild-type strain of A. nidulans, DHPA caused selective losses of microtubules, as determined by fluorescence microscopy, and of both α- and β-tubulins, as determined by Western blot analysis. This screening method involving the benA33 mutant of A. nidulans is useful, convenient, and highly selective. The phenylalanine derivatives tested are of a novel type of microtubule-disrupting antifungal agents, producing an accompanying loss of tubulins, and are different from well-known tubulin inhibitors affecting the assembly of tubulin dimers into microtubules