Good Night at 4 pm?! Time Expressions in Different Cultures (2022)

Abstract: We propose the task of culture-specific time expression grounding, i.e. mapping from expressions such as “morning” in English or “Manhã” in Portuguese to specific hours in the day. We propose 3 language-agnostic methods, one of which achieves promising results on gold standard annotations that we collected for a small number of languages. We then apply this method to 27 languages and analyze the similarities across languages in the grounding of time expressions.

## introduction natural language understanding requires the ability to map language such as color descriptions @xcite , spatial instructions @xcite , and gradable adjectives @xcite to real-world physical properties. this paper focuses on temporal grounding, particularly mapping time expressions such as "morning" and "evening" to hours in the day. temporal commonsense reasoning has been gaining traction lately @xcite , and this important capability can benefit various temporal tasks such as event ordering and duration prediction. one of the challenges in grounding time expressions to standard times is that such expressions may be interpreted with some variation by different people. @xcite found that human-written weather forecasts exhibited significant individual differences between forecasters in the interpretation of time expressions. one factor for this variation is cultural differences. @xcite analyzed the time of day in which people from 53 countries posted time-specific greetings such as "good morning" and "good evening" on twitter. they showed variation in greeting times across languages and cultures, which they connected to known facts and published statistics about cultural differences, such as differences in average wake and sleep times. we propose to re-frame the research question posed by @xcite as a task of time expression grounding: given a time expression, the goal is to map it to a range of hours during the day. for example, what is the range of hours an italian speaker refers to when saying pomeriggio (afternoon)? such a grounding model can provide cultural context to machine translation systems @xcite , language learning apps @xcite , and user-centered dialogue systems @xcite . we collected gold standard interpretations from four countries, which indeed exhibited some variation. we then proposed 3 language-agnostic methods based on either a corpus or a language model (lm). the corpus-based method performed well across languages, outperforming the method proposed by @xcite on 3 out of 4 languages. encouraged by the performance on the labelled languages, we applied the method to additional 23 unlabelled languages, and analyzed the differences predicted by the models. in the future, we plan to incorporate this method into nlp systems that may benefit from temporal grounding. areas of future work involve testing our methods on low-resource languages, as well as researching ways to overcome reporting bias @xcite : the under-representation of trivial facts in written text. we hope this work would be another small step in the long-term goal of developing culturally-aware nlp models @xcite ). foot_0 ## data we collected gold standard annotations for the start and end times of five time expressions: morning, noon, afternoon, evening, and night. the annotations were collected in amazon mechanical turk (amt) for english, hindi, italian, and portuguese. figure 1 : percents of native languages collected from each country. india is the only country where the majority native language differs from the language used in wikipedia and bert (hindi). numbers in brackets: we describe the rationale behind the choice of languages ( §2.1), the hit (human intelligence task) and annotation guidelines ( §2.2), and the observations from the collected data ( §2.3). ## methods we define the time expression grounding task: given a time expression, the goal is to predict its start and end times. we developed 3 methods that differ along two dimensions: (1) the source from which the times are learned: a corpus ( §3.1) or a language model ( §3.2); and (2) whether to compute start and end times directly or indirectly through estimating a distribution of times. ## discussion and conclusion we addressed the task of grounding time expressions such as "morning" and "noon" in different languages to explicit hours. our extractive method achieves good performance on languages for which we collected gold annotations. we dedicate the remainder of the paper to discuss various limitations and considerations for future work. temporal and seasonal factors. as discussed in §2.3, some workers mentioned that their interpretation of time expressions depends on the season, e.g., night starts earlier in the winter in the northern hemisphere. in addition, the time of day in which the workers answered the survey might have introduced some bias. the batches were published according to the authors' timezone and working hours, which might have been outside working hours for some countries. an early riser answering an amt survey at 5 am or a night owl that answers it at 2 am might not be representative of the population. finally, @xcite showed that tweets greeting "good morning" appeared later in the day during weekends and holidays, indicating later wake up times. it is possible that such factors will also affect the judgement of survey respondents.