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首页双语语义角色标签推理:双重分解与双向投影方法
"本文主要探讨了双语语义角色标注(Bilingual Semantic Role Labeling, 简称bilingual SRL)的问题,这是一种在平行双语文本中对语义角色进行注解的技术。由于双语SRL能充分利用两种语言的信息,它在机器翻译、跨语言信息检索等NLP任务中具有广泛应用。传统的做法是使用单语SRL系统分别处理双语文本的两边,但这可能导致两侧的标注结果不一致。为了克服这个问题,一些研究尝试联合推断双语SRL,利用双语文本中的互补语言提示来提升性能。 现有的双语SRL方法面临两个主要挑战:一是复杂的优化目标函数导致推理成本较高,二是完全依赖单语SRL系统产生的候选,可能丢失一些有价值的信息。针对这些问题,文章提出了双重分解(Lagrange Dual Decomposition)策略,以寻找双语文本两侧的一致性结果。同时,还引入了双向投影(Bi-directional Projection, BDP)方法,用于恢复单语SRL系统中被忽视的候选参数。 在标准的平行语料库OntoNotes数据集上,文章对提出的方法进行了评估,结果显示其性能优于最新的单语SRL系统。这表明双重分解和双向投影策略有效地解决了现有方法的局限性,提高了双语语义角色标注的一致性和准确性,对于双语信息处理和自然语言理解具有重要的理论和实践意义。" 这篇论文的核心贡献在于提出了一种新的双语语义角色标注方法,通过双重分解和双向投影技术,不仅降低了推理成本,而且提高了标注的一致性,这对于多语言环境下的自然语言处理任务有显著的改进效果。
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Bilingual Semantic Role Labeling Inference via Dual Decomposition 15:5
Fig. 3. An argument output of monolingual SRL systems: location loc, label l, and classification probability p.
2.3. Output of Monolingual SRL System
One reason why we choose a maximum entropy classifier as our classifier is that it can
easily incorporate arbitrary features. The other reason is that the maximum entropy
classifier can directly output classification probabilities without any postprocessing
that will be used in our joint inference stage. For clear illustration, we wrap the
classification probabilities into the output of monolingual SRL systems. As shown in
Figure 3, an argument output of monolingual SRL systems has three attributes: its
location in the sentence loc, represented by the position of its first word and last word;
its semantic role label l; and its classification probability p. Therefore, we annotate an
argument output of a monolingual SRL system as a triple (loc, l, p). For example, the
A0 argument in Figure 3 is ((6, 6), A0, 0.85).
2.4. Complementarity of Chinese and English SRL Systems
The semantic equivalence of the parallel bi-texts means that they should have the
same predicate semantic structure, which implies that the aligned argument pairs in
bi-texts (e.g., the pair of ‘’ and ‘Recently’ in Figure 1) should have consistent labels.
Nevertheless, the monolingual SRL systems of Chinese and English work indepen-
dently. Thus, some inconsistent cases often occur on both sides of monolingual SRL
systems. However, there are many complementarity clues that are beneficial to correct
these mistakes. For example, in Figure 1, if ‘’ is missed or annotated with a wrong
label by a Chinese SRL system, but ‘Recently’ is labeled correctly by an English SRL
system, intuitively, with the help of the English side, we can recognize the argument
of ‘’ on the Chinese side and give it a correct label.
From these observations, we can see that there are mainly two types of complemen-
tarity that can be utilized in the bilingual SRL system.
Location Complementarity means that, if one of an argument pair is found by the
monolingual SRL system but the other is missed, we can track down the missed ar-
gument in the other language according to word alignment information. It is worth
noting that there are also some exceptional cases, for example, the argument ellipsis
on one side. These cases have little impact on the location complementarity, however,
because, in these cases, the aligned points are null in the other language.
Label Complementarity means that an aligned argument pair in a bi-text should
have consistent labels. For instance, in Figure 1, the labels for ‘’ and ‘Recently’ are
AM-TMP, which are consistent. However, if the label on one side is wrong but the label
on the other side is right, we obtain inconsistent results for both sides. In these cases,
we should abandon this result and search for a pair of consistent results. It is worth
noting that the consistent labels for the aligned argument pair do not mean that they
have to have the same label. For a brief illustration, an example is given in here. In
ACM Trans. Asian Low-Resour. Lang. Inf. Process., Vol. 15, No. 3, Article 15, Publication date: December 2015.
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