{'fraction_non_alphanumeric': 0.014094955489614243, 'fraction_numerical': 0.0, 'mean_word_length': 5.516908212560386, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Helping users understand and recover from interpretation failures in natural language interfaces

When a person determines what he will say and how he will say it, he relies upon beliefs that he predicts to be shared with his audience. The difficulty of this inference task is amplified when a person speaks to a computer, because it is unclear what the computer knows and how the computer will use its knowledge to interpret the speaker’s utterance. This lack of transparency and predictably are barriers for natural language interfaces. Despite advances in natural language processing, interpretation failures are not going away. In human-human linguistic communication, ambiguity, vagueness and underspecification are frequent; and the listener is expected to be resilient and flexibly shift between different interpretations when needed, or diagnose when a discrepancy between the speaker and listener’s communication goals or beliefs are responsible for the communication failure. This paper takes the position that augmenting a NLI with nonlinguistic modalities may present a mode for recovering from interpretation failures that is richer than relying solely on dialogue. The paper presents a categorization of the levels of information where interpretation failures may occur, and explains the authors’ ongoing research using the framework.