Future Work

6.2.1 Enhance the sense classifier and center classifier

In this research, we build an end-to-end parsing model and mainly focus on handing discourse structure. In contrast, the mechanism to label relation sense and center is so far quite simple. In fact, there has been many researches working on relation sense labeling task, as mentioned in Chapter 2. Model enhancement like attention mechanism might be quite likely to improve the performance of relation sense and center labeling.

6.2.2 Fit the parsing model to the multi-way tree structure

As shown in Section 5.5 in Chapter 5, loss occurs when performing binary-to-multi-way transformation. It mainly due to the nature of multi-binary-to-multi-way discourse structure and our binary RvNN framework. There are two possible direction to reduce this performance reduction. One is to fit our model to the multi-way structure, and it requires modification of our RvNN unit and redesign of the CKY-like algorithm. Time complexity expansion due to this generalization of structure predicting may be a considered issue. Another direction is to optimize the transformation mechanism. Effects may be made to train the model to identify the correct nodes during the transformation.

6.2.3 Integrate syntactic information to build a syntactic-discourse jointly parsing model

In the last part of our experiments, we try to build a syntactic-discourse jointly parsing model. However, the performance is not satisfying on discourse parsing tasks. So far, we only integrate syntactic structure information when training the model while omitting the labels of each internal nodes of syntactic parsing tree. It is still a promising direction to build a model that organizes both syntactic and discourse information, and jointly parses the whole paragraph thoroughly from character level.

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