Question answering in Natural Language: the Special Case of Temporal Expressions (2021)

Abstract: Although general question answering has been well explored in recent years, temporal question answering is a task which has not received as much focus. Our work aims to leverage a popular approach used for general question answering, answer extraction, in order to find answers to temporal questions within a paragraph. To train our model, we propose a new dataset, inspired by SQuAD, a state-of-the-art question answering corpus, specifically tailored to provide rich temporal information by adapting the corpus WikiWars, which contains several documents on history’s greatest conflicts. Our evaluation shows that a pattern matching deep learning model, often used in general question answering, can be adapted to temporal question answering, if we accept to ask questions whose answers must be directly present within a text.

## introduction question answering is an automatic language processing task that aims to search for information in a text or database to answer a question in natural language. it is a task that differs from the query of a search engine, because it aims to exempt the user from querying the data using a formal language query. this type of method is particularly useful when the database is very large or poorly documented, or when the textual data to be queried is difficult to structure. general question answering is a subject that has already been widely explored, and we have therefore decided to focus on a particular part of this research domain. the purpose of this paper will be to explore question answering as it relates to temporal information in english texts. this is a task that can vary in difficulty: the temporal structure and the amount of temporal data can fluctuate quite a lot depending on the text, which can therefore be relatively simple to analyze or relatively complex, depending also on the questions asked. a set of clear definitions is therefore imperative. ## state of the art and methods traditional information retrieval involves finding a short passage of text within a set of documents. a selection of relevant documents is first made, then these documents are subdivided into sections, paragraphs, or sentences. we focus only the second part of this task, which we adapt to the case of temporal question answering. we decided to mainly use the information extraction method (vs the knowledge-base approach) which means using literal questions as we stated above, mainly because annotating the data is faster: when building the dataset, we can write the questions as they are without worrying about translating them into logical form. it is also possible to ask a third party to help build the dataset since all that is needed is to write a question and identify the answer within the text. these advantages make it possible to build a larger dataset more quickly. however, we are not opposed to the knowledge-base approach and we even think that combining the two approaches could be something to explore in the future. ## conclusion in this work, we chose to simplify the temporal question answering problem and limit our work to literal questions. we were thus able to apply an extractive approach with some success. indeed, this method allowed us to create a rather large dataset, annotated by hand, over a rather short period of time (only about a month and a half). thanks to this, we were also able to devote time to the state-of-the-art implementation of a deep machine learning model, the results of which demonstrated some of this model's strengths. nevertheless, it is not clear that the model is capable of understanding the underlying structure of the text it is dealing with. when the model has to choose between several temporal expressions, searching for the tokens closest to the answer does not always lead to the right prediction. moreover, our model is certainly not capable of making inferences and determining, for example, the date to which a deictic temporal expression, such as "this day", refers. in future work, we propose to broaden the definition of a temporal question in order to be able to deal with a larger variety of questions, especially inferential questions.