Do you want to publish a course? Click here

Applied Temporal Analysis: A Complete Run of the FraCaS Test Suite

التحليل الزمني التطبيقي: تشغيل كامل من جناح اختبار FRACAS

261   0   0   0.0 ( 0 )
 Publication date 2021
and research's language is English
 Created by Shamra Editor




Ask ChatGPT about the research

In this paper, we propose an implementation of temporal semantics that translates syntax trees to logical formulas, suitable for consumption by the Coq proof assistant. The analysis supports a wide range of phenomena including: temporal references, temporal adverbs, aspectual classes and progressives. The new semantics are built on top of a previous system handling all sections of the FraCaS test suite except the temporal reference section, and we obtain an accuracy of 81 percent overall and 73 percent for the problems explicitly marked as related to temporal reference. To the best of our knowledge, this is the best performance of a logical system on the whole of the FraCaS.



References used
https://aclanthology.org/
rate research

Read More

Split-and-rephrase is a challenging task that promotes the transformation of a given complex input sentence into multiple shorter sentences retaining equivalent meaning. This rewriting approach conceptualizes that shorter sentences benefit human read ers and improve NLP downstream tasks attending as a preprocessing step. This work presents a complete pipeline capable of performing the split-and-rephrase method in a cross-lingual manner. We trained sequence-to-sequence neural models as from English corpora and applied them to predict the transformations in English and Brazilian Portuguese sentences jointly with BERT's masked language modeling. Contrary to traditional approaches that seek training models with extensive vocabularies, we present a non-trivial way to construct symbolic ones generalized solely by grammatical classes (POS tags) and their respective recurrences, reducing the amount of necessary training data. This pipeline contribution showed competitive results encouraging the expansion of the method to languages other than English.
Temporal commonsense reasoning is a challenging task as it requires temporal knowledge usually not explicit in text. In this work, we propose an ensemble model for temporal commonsense reasoning. Our model relies on pre-trained contextual representat ions from transformer-based language models (i.e., BERT), and on a variety of training methods for enhancing model generalization: 1) multi-step fine-tuning using carefully selected auxiliary tasks and datasets, and 2) a specifically designed temporal masked language model task aimed to capture temporal commonsense knowledge. Our model greatly outperforms the standard fine-tuning approach and strong baselines on the MC-TACO dataset.
Participation in inter-laboratory comparison programs is an important means of laboratory quality control and assessing laboratory performance, and these programs can be used by customers or regulatory bodies for the selection of qualified laborato ries. This research describes how to use inter-comparison tests and how to statistically analyse the test results. This research has a practical study of assessing laboratories performance in laboratories of the Syrian textile firms by distributing samples simultaneously to participating laboratories for testing. After collecting test results, the researcher used scientific methods to handle data to identify the weak points in laboratories performance and provide them the Feedback and technical advice to Assistance the lab to defining the measurement problems and evaluating of test methods and instrumentation , and could introduce some suggestions and recommendations to overcome.
Performance of neural models for named entity recognition degrades over time, becoming stale. This degradation is due to temporal drift, the change in our target variables' statistical properties over time. This issue is especially problematic for so cial media data, where topics change rapidly. In order to mitigate the problem, data annotation and retraining of models is common. Despite its usefulness, this process is expensive and time-consuming, which motivates new research on efficient model updating. In this paper, we propose an intuitive approach to measure the potential trendiness of tweets and use this metric to select the most informative instances to use for training. We conduct experiments on three state-of-the-art models on the Temporal Twitter Dataset. Our approach shows larger increases in prediction accuracy with less training data than the alternatives, making it an attractive, practical solution.

suggested questions

comments
Fetching comments Fetching comments
Sign in to be able to follow your search criteria
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا