In this final section, we summarize future directions led by our work.
The instantiation relation. One aspect that we haven’t explored when characterizing instantiation (Section 3.2) is textual entailment. As pointed out in Section 3.2.4, even
though in theory the second argument of instantiation should entail the first, few in-
stances of the relation in the PDTB are automatically recognized as entailment. We found that the entailment relationship appears to be at phrase or clause levels, and often depends on context and external knowledge. We believe future work exploring these directions can
benefit RTE systems and instantiationrecognition.
When comparinginstantiationwithspecification, we pointed out that thechangein
specificity acrossspecificationarguments may not be as large as that in instantiation,
especially if the first argument of specification does not need to be particularly general or the second argument particularly specific (Section 3.4). This hypothesis, if confirmed, will bring new insight into discourse relations and specificity. We leave for future work to have a fair judgement of this hypothesis, using a measure of specificity independent of either relations (e.g., via human judgements, such as that outlined in Section 4.1).
Subsentential specificity. We present an annotation guideline and a pilot corpus to annotate the degree of sentence specificity, and the cause and effect of underspecified text (Section 4.1). In this annotation, we did not separate if an underspecified text segment is elaborated in upcoming context or not in upcoming context. We also leave for future work to analyze the content of the underspecified segments and their associated questions, which can be useful for gaining further insights into what needs elaboration and what causes vagueness.
Our work proposes the first model to predict underspecified words within a sentence (Section 4.2). As pointed out in Section 4.3, there are multiple ways to improve the model, for example, to train on more data so that more powerful models can be adopted, to gain sharper attention weights using repeated attention, and to obtain structure on top of the current token-level prediction with structured attention. Future work can also tackle the
prediction of the number of underspecified tokens, along with how and where they can be resolved.
Cross-lingual analysis. In Section 5.1, we pointed out that both Arabic and Chinese
have sentences that need multiple English sentences to translate. However we did not
find these sentences to be especially problematic for Arabic-English translation. Future work can explore this negative result, and uncover linguistic constructs that lead to this contrasting finding between Arabic and Chinese. Future work can also look into more languages, especially those with more extreme differences in punctuation usage (e.g., Thai). To identify content-heavy sentences in Chinese which need multiple English sentences
to translate, we developed a system with rich syntactic features. We also pointed out
differences in discourse relation distribution across split components in a heavy sentence vs. those not involved in splitting (Section 6.3.3). As pointed out in Section 5.3, one obvious future direction is to incorporate the insight from our work to improve Chinese to English machine translation.
In terms of specificity, we discovered strong associations between content-heavy Chinese sentences, text specificity and the second argument ofinstantiation(Chapter 6). Future
work can further explore specificity across different languages, e.g., sentence specificity prediction in Chinese.
Specificity and sentence simplification. Section 7.1 shows strong associations between specificity and simplified sentences. When characterizing sentences that need simplification, specificity is as indicative as and complementary to readability. Furthermore, we found that often the simplified version of a sentence uses multiple sentences to express the content in the original. We leave to future work to incorporate specificity and our insights in content- heavy sentences into sentence simplification systems.
Specificity and demographics. Section 7.2 presents our pilot study exploring specificity perception variation across varying autism symptoms. While our study did not lead to statistically significant findings, we pointed out several ways to improve the experiment: recruiting subjects with clinically diagnosed ASD, expanding the applicability of our stimuli
and extending our analysis on subject-produced summaries.
Our work exploring links between specificity and gender, reading abilities and autism symptoms opens new directions for future work to go further into aspects in socio-demographics and personal background (Section 7.3). Research in social media text has found distinctive language usage across people of different genders, income levels, personality and political views. We leave for future work to investigate how specificity is perceived and organized when these aspects vary.
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