Experimental results show that our method obtained the highest correlations among the methods in both sentence-level adequacy and fluency.

Experimental results demonstrate that our method yields the highest correlation among eight methods in terms of sentence-level adequacy and fluency.

Future studies Will improve our method, enabling it to achieve high correlation in sentence-level fluency.

We calculated Pearson’s correlation efficient and Spearman’s rank correlation efficient between the scores obtained using our method and the scores by human judgments in terms of sentence-level adequacy and fluency.

Tables 2 and 3 respectively show Pearson’s correlation coefficient for sentence-level adequacy and fluency.

Tables 4 and 5 respectively show Spearman’s rank correlation coefficient for sentence-level adequacy and fluency.

However, sentence-level automatic evaluation is insufficient.

As described herein, for use with MT systems, we propose a new automatic evaluation method using noun-phrase chunking to obtain higher sentence-level correlations.

Evaluation experiments using MT outputs obtained by 12 machine translation systems in NTCIR—7(Fujii et al., 2008) demonstrate that the scores obtained using our system yield the highest correlation with the human judgments among the automatic evaluation methods in both sentence-level adequacy and fluency.

Experiments show that document-level information can improve the performance of a sentence-level baseline event extraction system.

In this section we present our approach to using document-level event and role information to improve sentence-level ACE event extraction.

Our event extraction system is a two-pass system where the sentence-level system is first applied to make decisions based on local information.

5.1 Sentence-level Baseline System

We use the rest of the ACE training corpus (549 documents) as training data for both the sentence-level baseline event tagger and document-level event tagger.

Recall improved sharply, demonstrating that cross-event information could recover information that is difficult for the sentence-level baseline to extract; precision also improved over the baseline, although not as markedly.

We analyzed the sentence-level baseline event extraction, and found that many events are missing or spuriously tagged because the local information is not sufficient to make a confident decision.

Rather than recruiting annotators for marking span pairs, we modify the parsing algorithm in Section 3 so as to produce span pair annotation out of sentence-level annotation.

In the base step, only the word pairs listed in sentence-level annotation are inserted in the hypergraph, and the re-cursive steps are just the same as usual.

If the sentence-level annotation satisfies the alignment constraints of ITG, then each F-span will have only one E-span in the parse tree.

where BLEU(e,-j, r,-) is the smoothed sentence-level BLEU score (Liang et al., 2006) of the translation e with respect to the reference translations r,, and e: is the oracle translation which is selected from {em ..., em} in terms of BLEU(e,-j, r,-).

In this work, a sentence-level combination method is used to select the best translation from the pool of the n-best outputs of all the member systems.

In this work, we use a sentence-level system combination method to generate final translations.

sentence-level combination (Hildebrand and Vogel, 2008) simply selects one from original translations, while some more sophisticated methods, such as word-level and phrase-level combination (Matusov et al., 2006; Rosti et al., 2007), can generate new translations differing from any of the original translations.

In the following, we will structure this feature space along two dimensions, distinguishing NP- and sentence-level factors as well as syntactic and semantic (including lexical semantic) factors.

Sentence-level features are extracted from the clause (in which the NP appears), as well as sentential and non-sentential adjuncts of the clause.

Using syntactic features on the NP-or sentence-level only, however, leads to a drop in precision as well as recall.