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In this paper, a two-level deep learning framework is presented to model human information foraging behavior with search engines. A recurrent neural network architecture is designed using LSTM as the base unit to explicitly consider the temporal and spatial dependencies of information scents, the key concept in Information Foraging Theory. The target is to predict several major search behaviors, such as query abandonment, query reformulation, number of clicks, and information gain. The memory capability and the sequence structure of LSTM allow to naturally mimic not only what users are perceiving and performing at the moment but also what they have seen and learned from the past during the search dynamics. The promising results indicate that our information scent models with different input variations were better, compared to the state-of-the art neural click models, at predicting some search behaviors. When incorporating the knowledge from a previous query in the same search session, the prediction of current query abandonment, pagination, and information gain has been improved. Compared to the well known neural click models that model search behaviors under a single search query thread, this study takes a broader view to consider an entire search session which may contain multiple queries. More importantly, our model takes the search result relevance pattern on the Search Engine Results Pages (SERP) as a whole as the information scent input to the deep learning model, instead of considering one search result at each step. The results have insights on the impact of information scents on how people forage for information, which has implications for designing or refining a set of design guidelines for search engines.
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Modeling morphosyntactic agreement as neural search: a case study of Hindi-Urdu
Agreement is central to the morphosyntax of many natural languages. Within contemporary linguistic theory, agreement relations have often been analyzed as the result of a structure-sensitive search operation. Neural language models, which lack an explicit bias for this type of operation, have shown mixed success at capturing morphosyntactic agreement phenomena. This paper develops an alternative neural model that formalizes the search operation in a fully differentiable way using gradient neural attention, and evaluates the model's ability to learn the complex agreement system of Hindi-Urdu from a large-scale dependency treebank and smaller synthetic datasets. We find that this model outperforms standard architectures at generalizing agreement patterns to held-out examples and structures.
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- Award ID(s):
- 1941593
- PAR ID:
- 10560507
- Publisher / Repository:
- University of Massachusetts Amherst Libraries
- Date Published:
- Journal Name:
- Proceedings of the Society for Computation in Linguistics
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2834-1007
- Page Range / eLocation ID:
- 227–239
- Subject(s) / Keyword(s):
- agreement morphosyntax computational modeling neural networks Hindi-Urdu
- Format(s):
- Medium: X
- Right(s):
- Creative Commons Attribution 4.0
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.7275/scil.2148
