Integer Linear Programming for Dutch Sentence Compression

Belder, Jan De; Moens, Marie‐Francine

doi:10.1007/978-3-642-12116-6_60

Cited by 3 publications

(4 citation statements)

References 14 publications

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“…However, they focus on improving the prompt format, while our work focuses on selecting good exemplars. Integer Linear Programming for NLP Integer Linear Programming has been used in many NLP tasks (Martins, 2014), including coreference resolution (Denis and Baldridge, 2007;De Belder and Moens, 2012), sentence compression (De Belder and Moens, 2010), dependency parsing (Riedel and Clarke, 2006), semantic role labeling (Roth and Yih, 2005), and translation (Germann et al, 2004). In this work, we introduce a novel application of ILP in NLP: exemplar selection for ICL.…”

Section: Related Workmentioning

confidence: 99%

“…The objective function consists of a weighted linear combination of variables, while the constraints are (in)equalities that involve linear combinations of these variables. These constraints serve to restrict the value ranges of the variables and capture their interaction effects (De Belder and Moens, 2010). Integer Linear Programming (ILP) is a subset of LP, wherein variables are constrained to take only integer values.…”

Section: Integer Linear Programmingmentioning

confidence: 99%

“…The Knapsack is NP-hard, but several algorithms have been developed to find an optimal solution efficiently, including Branch-and-Bound and Cutting Planes (De Belder and Moens, 2010). Several solvers provide efficient implementations of these algorithms (Martins, 2014).…”

Section: Integer Linear Programmingmentioning

confidence: 99%

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SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQA

Tonglet,

Reusens,

Borchert

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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Question answering over hybrid contexts is a complex task, which requires the combination of information extracted from unstructured texts and structured tables in various ways. Recently, In-Context Learning demonstrated significant performance advances for reasoning tasks. In this paradigm, a large language model performs predictions based on a small set of supporting exemplars. The performance of In-Context Learning depends heavily on the selection procedure of the supporting exemplars, particularly in the case of HybridQA, where considering the diversity of reasoning chains and the large size of the hybrid contexts becomes crucial. In this work, we present Selection of ExEmplars for hybrid Reasoning (SEER), a novel method for selecting a set of exemplars that is both representative and diverse. The key novelty of SEER is that it formulates exemplar selection as a Knapsack Integer Linear Program. The Knapsack framework provides the flexibility to incorporate diversity constraints that prioritize exemplars with desirable attributes, and capacity constraints that ensure that the prompt size respects the provided capacity budgets. The effectiveness of SEER is demonstrated on FinQA and TAT-QA, two real-world benchmarks for HybridQA, where it outperforms previous exemplar selection methods 1 .

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Section: Related Workmentioning

confidence: 99%

Section: Integer Linear Programmingmentioning

confidence: 99%

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SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQA

Tonglet,

Reusens,

Borchert

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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“…Query suggestions with topics of interest for children and teenagers derived from social media has been proposed by Duarte Torres et al [2012]. Language simplification methods based on linear programming optimization have also been proposed to reduce the lexical and grammatical complexity of short texts (in Dutch) [De Belder and Moens 2010]. A framework for interaction with touch interfaces has been proposed by van Dijk et al [2012].…”

Section: Browsing Behaviormentioning

confidence: 99%

Analysis of Search and Browsing Behavior of Young Users on the Web

2014

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The Internet is increasingly used by young children for all kinds of purposes. Nonetheless, there are not many resources especially designed for children on the Internet and most of the content online is designed for grown-up users. This situation is problematic if we consider the large differences between young users and adults since their topic interests, computer skills, and language capabilities evolve rapidly during childhood. There is little research aimed at exploring and measuring the difficulties that children encounter on the Internet when searching for information and browsing for content. In the first part of this work, we employed query logs from a commercial search engine to quantify the difficulties children of different ages encounter on the Internet and to characterize the topics that they search for. We employed query metrics (e.g., the fraction of queries posed in natural language), session metrics (e.g., the fraction of abandoned sessions), and click activity (e.g., the fraction of ad clicks). The search logs were also used to retrace stages of child development. Concretely, we looked for changes in interests (e.g., the distribution of topics searched) and language development (e.g., the readability of the content accessed and the vocabulary size). In the second part of this work, we employed toolbar logs from a commercial search engine to characterize the browsing behavior of young users, particularly to understand the activities on the Internet that trigger search. We quantified the proportion of browsing and search activity in the toolbar sessions and we estimated the likelihood of a user to carry out search on the Web vertical and multimedia verticals (i.e., videos and images) given that the previous event is another search event or a browsing event. We observed that these metrics clearly demonstrate an increased level of confusion and unsuccessful search sessions among children. We also found a clear relation between the reading level of the clicked pages and characteristics of the users such as age and educational attainment. In terms of browsing behavior, children were found to start their activities on the Internet with a search engine (instead of directly browsing content) more often than adults. We also observed a significantly larger amount of browsing activity for the case of teenager users. Interestingly we also found that if children visit knowledge-related Web sites (i.e., information-dense pages such as Wikipedia articles), they subsequently do more Web searches than adults. Additionally, children and especially teenagers were found to have a greater tendency to engage in multimedia search, which calls to improve the aggregation of multimedia results into the current search result pages.

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