Increasing Sentence-Level Comprehension Through Text Classification of Epistemic Functions (2021)

Abstract: Word embeddings capture semantic meaning of individual words. How to bridge word-level linguistic knowledge with sentence-level language representation is an open problem. This paper examines whether sentence-level representations can be achieved by building a custom sentence database focusing on one aspect of a sentence’s meaning. Our three separate semantic aspects are whether the sentence: (1) communicates a causal relationship, (2) indicates that two things are correlated with each other, and (3) expresses information or knowledge. The three classifiers provide epistemic information about a sentence’s content.

## introduction in reading comprehension, the sum is greater than the parts. a strong reader combines the reader's prior knowledge, reasoning ability, and the text's substance to reason about the events, entities, and their relations across a full document @xcite . in essence, reading comprehension requires the reader to develop high levels of abstraction. @xcite each classification task introduced in the paper, if mastered by a computer model, allow a model to comprehend a sentence's meaning at greater level of abstraction than comparing word meaning similarity between sentences. with this new ability, the model should be able to master more complex downstream tasks than a model limited to wordlevel comprehension @xcite . ## conclusion to classify sentences based upon their epistemic functions, we created a new data set 14 containing 8,327 sentences that we annotated for whether a sentence communicates a causal or correlating relationship, and whether it expresses information or knowledge. utilizing bert, the three epistemic function models achieved sufficient accuracy that future work should include determining the models' abilities to solve real world problems. in that vein, we plan to utilize our models for a range of 14 available at: https:// github.com/goldstein-berger/ law-dmr21-sentence-meaning-annotation downstream tasks such as event detection, question answering, and discourse parsing. our approach may be able to improve upon these tasks by providing the underlying model additional features through which to comprehend the text in a more nuanced fashion. in addition, as the snorkel approach appears promising, we plan in future work to determine if there is a systematic method, which can be utilized to determine the underlying functions needed to gather the weakly-supervised data. if this can be accomplished, then a lesser amount of gold data may be needed. as gold data is generally the biggest bottleneck that nlp practitioners face, a systematic approach to snorkel may allow for a significant time reduction for building future nlp models.