To Schedule or not to Schedule: Extracting Task Specific Temporal Entities and Associated Negation Constraints

• URL: http://arxiv.org/abs/2012.02594v1
• Date: Sun, 15 Nov 2020 10:07:19 GMT
• Title: To Schedule or not to Schedule: Extracting Task Specific Temporal Entities and Associated Negation Constraints
• Authors: Barun Patra, Chala Fufa, Pamela Bhattacharya and Charles Lee
• Abstract summary: State of the art research for date-time entity extraction from text is task agnostic. We show the efficacy of our method on the task of date-time understanding in the context of scheduling meetings for an email-based digital AI scheduling assistant.
• Score: 2.053142696037897
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: State of the art research for date-time entity extraction from text is task agnostic. Consequently, while the methods proposed in literature perform well for generic date-time extraction from texts, they don't fare as well on task specific date-time entity extraction where only a subset of the date-time entities present in the text are pertinent to solving the task. Furthermore, some tasks require identifying negation constraints associated with the date-time entities to correctly reason over time. We showcase a novel model for extracting task-specific date-time entities along with their negation constraints. We show the efficacy of our method on the task of date-time understanding in the context of scheduling meetings for an email-based digital AI scheduling assistant. Our method achieves an absolute gain of 19\% f-score points compared to baseline methods in detecting the date-time entities relevant to scheduling meetings and a 4\% improvement over baseline methods for detecting negation constraints over date-time entities.

Related papers

• Efficient Conformance Checking of Rich Data-Aware Declare Specifications (Extended) [49.46686813437884]
  We show that it is possible to compute data-aware optimal alignments in a rich setting with general data types and data conditions.<n>This is achieved by carefully combining the two best-known approaches to deal with control flow and data dependencies.
  arXiv  Detail & Related papers  (2025-06-30T10:16:21Z)
• DATETIME: A new benchmark to measure LLM translation and reasoning capabilities [0.0]
  This paper introduces DATETIME, a new high-quality benchmark designed to evaluate the translation and reasoning abilities of a Large Language Model (LLM) on datetimes. Datetimes are intuitive and straightforward for humans to process but present significant challenges for LLMs. Our experiments show that state-of-the-art models exhibit significant difficulty with tasks involving reasoning on datetimes.
  arXiv  Detail & Related papers  (2025-04-22T17:52:04Z)
• Domain-Independent Automatic Generation of Descriptive Texts for Time-Series Data [5.264562311559751]
  We propose a method to generate domain-independent descriptive texts from time-series data. By implementing the novel backward approach, we create the Temporal Automated Captions for Observations dataset. Experimental results demonstrate that a contrastive learning based model trained using the TACO dataset is capable of generating descriptive texts for time-series data in novel domains.
  arXiv  Detail & Related papers  (2024-09-25T06:04:03Z)
• Handling Numeric Expressions in Automatic Speech Recognition [56.972851337263755]
  We compare cascaded and end-to-end approaches to recognize and format numeric expression. Results show that adapted end-to-end models offer competitive performance with the advantage of lower latency and inference cost.
  arXiv  Detail & Related papers  (2024-07-18T09:46:19Z)
• DOLOMITES: Domain-Specific Long-Form Methodical Tasks [81.63464319950664]
  We develop a typology of methodical tasks structured in the form of a task objective, procedure, input, and output. We introduce DoLoMiTes, a novel benchmark with specifications for 519 such tasks elicited from hundreds of experts from across 25 fields. Our benchmark further contains specific instantiations of methodical tasks with concrete input and output examples.
  arXiv  Detail & Related papers  (2024-05-09T17:25:31Z)
• A Dataset for the Validation of Truth Inference Algorithms Suitable for Online Deployment [76.04306818209753]
  We introduce a substantial crowdsourcing annotation dataset collected from a real-world crowdsourcing platform. This dataset comprises approximately two thousand workers, one million tasks, and six million annotations. We evaluate the effectiveness of several representative truth inference algorithms on this dataset.
  arXiv  Detail & Related papers  (2024-03-10T16:00:41Z)
• TIMELINE: Exhaustive Annotation of Temporal Relations Supporting the Automatic Ordering of Events in News Articles [4.314956204483074]
  This paper presents a new annotation scheme that clearly defines the criteria based on which temporal relations should be annotated. We also propose a method for annotating all temporal relations -- including long-distance ones -- which automates the process. The result is a new dataset, the TIMELINE corpus, in which improved inter-annotator agreement was obtained.
  arXiv  Detail & Related papers  (2023-10-26T22:23:38Z)
• PromptRE: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming [30.597623178206874]
  We propose PromptRE, a novel weakly-supervised document-level relation extraction method. PromptRE incorporates the label distribution and entity types as prior knowledge to improve the performance. Experimental results on ReDocRED, a benchmark dataset for document-level relation extraction, demonstrate the superiority of PromptRE over baseline approaches.
  arXiv  Detail & Related papers  (2023-10-13T17:23:17Z)
• Automated Few-shot Classification with Instruction-Finetuned Language Models [76.69064714392165]
  We show that AuT-Few outperforms state-of-the-art few-shot learning methods. We also show that AuT-Few is the best ranking method across datasets on the RAFT few-shot benchmark.
  arXiv  Detail & Related papers  (2023-05-21T21:50:27Z)
• Curriculum-Based Self-Training Makes Better Few-Shot Learners for Data-to-Text Generation [56.98033565736974]
  We propose Curriculum-Based Self-Training (CBST) to leverage unlabeled data in a rearranged order determined by the difficulty of text generation. Our method can outperform fine-tuning and task-adaptive pre-training methods, and achieve state-of-the-art performance in the few-shot setting of data-to-text generation.
  arXiv  Detail & Related papers  (2022-06-06T16:11:58Z)
• Truth Discovery in Sequence Labels from Crowds [12.181422057560201]
  Crowdsourcing platforms, such as Amazon Mechanical Turk (AMT), have been deployed to assist in this purpose. Existing literature in annotation aggregation assumes that annotations are independent and thus faces challenges when handling the sequential label aggregation tasks. We propose an optimization-based method that infers the ground truth labels using annotations provided by workers for sequential labeling tasks.
  arXiv  Detail & Related papers  (2021-09-09T19:12:13Z)
• The Contextual Appointment Scheduling Problem [4.644923443649425]
  We formulate ASP as an Integrated Estimation and Optimization problem using a task-based loss function. We justify the use of contexts by showing that not including the them yields to inconsistent decisions, which translates to sub-optimal appointments.
  arXiv  Detail & Related papers  (2021-08-12T04:51:26Z)
• Partially-Aligned Data-to-Text Generation with Distant Supervision [69.15410325679635]
  We propose a new generation task called Partially-Aligned Data-to-Text Generation (PADTG) It is more practical since it utilizes automatically annotated data for training and thus considerably expands the application domains. Our framework outperforms all baseline models as well as verify the feasibility of utilizing partially-aligned data.
  arXiv  Detail & Related papers  (2020-10-03T03:18:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.