Question: What do the authors do with the dataset?
Evidence: The advent of smart agents such as Amazon Echo and Google Home has shown relatively wide market adoption. Users have been familiarized with formulating questions and orders in a way that these agents can easily comprehend and take actions. Given this trend, particularly for cases where questions can have various forms such as yes/no, alternative, wh-, echo and embedded BIBREF0, a number of analysis techniques have been studied in the domain of semantic role labeling BIBREF1 and entity recognition BIBREF2. Nowadays, various question answering tasks have been proposed BIBREF3 and have yielded systems that have demonstrated significant advances in performance. Studies on the parsing of canonical imperatives BIBREF4 have also been done for many household agents.
However, discerning the intent from a conversational and non-canonical sentence (question or command) and extracting its intent argument is still a challenge. Additional complexity is introduced when the target text is in a speech recognition context, as the result may not contain punctuation. For example, given an unclear declarative question BIBREF5 such as “poppa joe you want me to go now”, a human listener can interpret the question as `if Joe wants the speaker to go now', but this can be challenging to for a machine. Also, sometimes, merely the speech act can be hard to guess from the sentence form, as in inferring “why don't you just call the police” as a representation of the to-do list `to call the police' (Figure 1). Although many advanced dialog managing systems may generate a plausible reaction to the input utterances, it is different from extracting the exact intent argument (a question set or a to-do-list) that should be investigated for an actual operation.
Complexities like the example discussed above have not seen much exploration outside of English, especially in the context of languages with a distinguished syntax or cases which do not use Latin-like alphabets. As a more concrete example, in the Korean language, the morphology is agglutinative, the syntax is head-final, and scrambling (non-deterministic permutations of word/phrase ordering) is a common practice between native speakers. Specifically, the agglutinative property of Korean requires additional morphological analysis, which makes it challenging to identify the component of the sentence that has the strongest connection to core intent. Additionally, the head-finality characteristic introduces an additional layer of complexity, where an under-specified sentence ender incorporates a prosodic cue which requires disambiguation to comprehend the original intent BIBREF6, BIBREF7. Finally, considering the scrambling aspect, which frequently happens in spoken utterances, further analysis is required on top of recognizing the entities and extracting the relevant phrases. This makes it difficult for dialog managers to directly apply conventional analysis methods that have been used in Germanic or other Indo-European languages.
In this paper, we explore these aspects in the context of Korean, a less explored, low-resource language with various non-canonical expressions. From there on, we propose a structured sentence annotation scheme which can help enrich the human-like conversation with artificial intelligence (AI). For the automation, we annotate an existing corpus and then augment the dataset to mitigate class imbalance, demonstrating the flexibility, practicality, and extensibility of the proposed methods. To further prove that the scheme is not limited to Korean, we demonstrate the methodology using English examples and supplement specific cases with Korean. To begin with, in section 2, we present the theoretical background of this study. We then discuss the detailed procedure with examples, along with an explanation of how it fits with modern natural language understanding (NLU) systems and an evaluation framework.
For the automation, we annotate an existing corpus and then augment the dataset to mitigate class imbalance, demonstrating the flexibility, practicality, and extensibility of the proposed methods.
Answer: The authors annotate an existing corpus and then augment the dataset to mitigate class imbalance, demonstrating the flexibility, practicality, and extensibility of the proposed methods.