In this paper, we propose a novel reordering model based on sequence labeling techniques.

Our model converts the reordering problem into a sequence labeling problem, i.e.

By our method, the reordering problem is converted into a sequence labeling problem so that the whole source sentence is taken into consideration for reordering decision.

We choose CRFs and RNN to accomplish the sequence labeling task.

The main contributions of the paper are: propose the tagging-style reordering model and improve the translation quality; compare two sequence labeling techniques CRFs and RNN; compare our method with seven other reordering models.

Our model converts the decoding order problem into a sequence labeling problem, i.e.

Now Figure 1(d) converts the reordering problem into a sequence labeling or tagging problem.

For the baseline system, we used the CRF++1 implementation of CRF sequence labeling with default parameters.

Sequence labeling algorithms (ex.

Benajiba and Rosso (2008) used CRF sequence labeling and incorporated many language specific features, namely POS tagging, base-phrase chunking, Arabic tokenization, and adjectives indicating nationality.

sequence labeling and a Support Vector Machine (SVM) classifier.

The use of CRF sequence labeling for NER has shown success (McCallum and Li, 2003; Nadeau and Sekine, 2009; Benajiba and Rosso, 2008).

We formulate surface realisation as a sequence labelling task and combine the use of conditional random fields (CRFs) with semantic trees.

The main idea of our approach is to treat surface realisation as a sequence labelling task in which a sequence of semantic inputs needs to be labelled with appropriate surface realisations.

We have presented a novel technique for surface realisation that treats generation as a sequence labelling task by combining a CRF with tree-based semantic representations.

In addition, we may compare different sequence labelling algorithms for surface realisation (Nguyen and Guo, 2007) or segmented CRFs (Sarawagi and Cohen, 2005) and apply our method to more complex surface realisation domains such as text generation or summarisation.

In this paper, we propose to formulate surface realisation as a sequence labelling task.

We regard this task as a sequence labeling problem and use Conditional Random Fields (CRFs) (Laf-ferty et al., 2001) as a machine learning framework.

Fig 2 shows an example of such sequence labeling .

Although this example is about sequential labeling shown on English sentences for ease of explanation, it was actually done on Japanese sentences.

To meet this challenge, we developed a sequence labeling method that identifies not only intra-sentential causal relations, i.e., the causal relations between two terms/phrases/clauses expressed in a single sentence (e.g., A1 in Table 1), but also the inter-sentential causal relations, which are the causal relations between two terms/phrases/clauses expressed in two adjacent sentences (e.g., A2) in a given text fragment.

Hence, rather than pipeline the two processes serially as previous work, we formulate it as a two-layer sequential labeling problem.

Given the general performance and discrimi-native framework, Conditional Random Fields (CRFs) (Lafferty et al., 2001) is a suitable framework for tackling sequence labeling problems.

CRFs represent a basic, simple and well-understood framework for sequence labeling , making it a suitable framework for adapting to perform joint inference.

Character-based sequence labeling is employed for word segmentation due to its simplicity and robustness to the unknown word problem (Xue, 2003).

Sequence labeling models have been successfully employed to identify opinion expressions (e.g.

We formulate the task of opinion entity identification as a sequence labeling problem and employ conditional random fields (CRFs) (Lafferty et al., 2001) to learn the probability of a sequence assignment y for a given sentence x.

J ohansson and Moschitti (2010a) extract opinion expressions and holders by applying reranking on top of sequence labeling methods.

Recent studies show that character sequence labeling is an effective formulation of Chinese word segmentation (Low et al., 2005; Zhao et al., 2006a,b; Chen et al., 2006; Xue, 2003).

Besides, Sun and Xu (2011) uses a sequence labeling framework, while unsupervised statistics are used as discrete features in their model, which prove to be effective in Chinese word segmentation.

Sun and Xu (2011) uses punctuation information as discrete feature in a sequence labeling framework, which shows improvement compared to the pure sequence labeling approach.