sequence tagger, with the tagger responsible for locating chunk boundaries and the classifier linking them to one of the templates.
6.3
Processing with semantic chunking
Apart from the three chunking models described in the previous section, the contributions of this thesis include a range of ideas for applying semantic chunking to downstream tasks. The main recurring target we focused on was realization from DMRS, using finite clause chunks of the rule-based chunker and the wider variety offered by the scopal system. The processing paradigm associated with chunking is the divide-and-conquer approach, in which partial results based on semantic chunks are combined in the end step into the output for the full sentence. Both sets of realization experiments (§3.6.3, §5.5) illustrate how the assembled results can match the output of full processing in quality, while offering considerable performance benefits.
The majority of semantic content of the sentence is expressed in the semantic chunks themselves, but the information about how they combine into the full sentence is not included. We store the associated information in functional fragments, which often are not well-formed fragments of representation. In DMRS they are DMRS subgraphs with missing compulsory arguments or even consisting of only links (e.g. for clausal complements), while in surface strings they can correspond to disjoint token spans. The challenge of the assembly step lies in the faithful and efficient inclusion of the information from functional fragments into the final result.
In this thesis we proposed two methods of incorporating the functional information. The functional subgraphs of the rule-based chunking model from Chapter 3 were realized using small placeholder DMRS graphs with known surface representations (§3.6.1). We enhanced the functional graphs with placeholders at their points of contact with semantic chunks, realized the resulting well-formed DMRS and replaced the known placeholder strings with the realization results of actual chunks. The approach worked because of the strong constraint on the form of chunks, as finite clauses are to large extent grammatically interchangeable.
The wider variety of chunks produced by the second DMRS-focused model makes the placeholder approach impractical. Each type of chunk, as defined by its nodes participating in the template, would require a dedicated placeholder. Instead, we put to use the generalization capabilities of the new framework and extracted the representations of functional fragments from the training data into surface templates (§5.4.4), sampling the DMRS graphs in order to decouple multiple templates applicable to the same sentence (§5.4.4). Whether or not
we could identify the surface pattern associated with each template served as an additional criterion on whether the given chunking decision was appropriate for realization.
The final practical contribution of this thesis is the transfer of semantic chunks between representations (§4.1). We successfully demonstrated how a chunking model defined on one representation can be used to bootstrap a system for another one on the example of DMRS graphs and surface strings. As suggested in §6.1.1, semantic chunking of DMRS can also inform similar models using other graph-based representations. The surface chunks produced by the transfer were evaluated using an intrinsic evaluation procedure (§4.2), which is less computationally costly than retraining the full model. To accompany the evaluation, we designed an F-score comparison metric (§4.2.2) for inexact graph matching on DMRS (§4.2.1). The measure also provided a filtering criterion for the training data for the sequence labelling model.
6.3.1
Further research on processing with semantic chunking
The practical solutions to semantic chunking were exemplified in this thesis with the target task of realization. It was chosen because of its close relationship to both types of represen- tation we work with and the demanding requirements of high precision in its inputs. The surface semantic chunks created in Chapter 4, however, remain to be evaluated on the target application of parsing. Other tasks that can benefit from surface chunking include machine translation and summarisation, both particularly dependent on semantic content of their inputs. The DMRS-based models can directly assist existing applications within the *MRS framework, e.g. HSSR/T (§5.3.1) or the summarisation system by Fang et al. (2016).
The difficult question of how to best incorporate the functional fragments into the construction of full results is another area of potential focused research. The processing of the fragments should not incur significant computational costs and should avoid redundancies, e.g. due to processing the same DMRS node multiple times. One of the improvements we suggest is the extraction of predicate-span information from the generator, so that each token in the realization result can be traced back to the node which produced it (cf. §5.4.4). This would allow for a better treatment of sentential modifiers and remove the need for English-centric left-to-right assumptions on which we rely in our experiments. At the same time, the node-token association would potentially open a way for disjoint surface semantic chunks and functional fragments.
The final addition to the experiments could target realization ranking. ACE ranks its outputs with a maximum entropy model trained on treebanked data. In §3.6 we assigned the final output a score by summing the logarithms of scores of individual chunk realizations, while in §5.5 we simply combined the best scoring version of the surface of each chunk.