Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAutomatic rule generation for time expression normalization
توليد القاعدة التلقائي لتطبيع التعبير
308
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
يشمل فهم تعبيرات الوقت بمهام فرعية: الاعتراف والتطبيع. في السنوات الأخيرة، تم إحراز تقدم كبير في الاعتراف بعصائر الوقت أثناء تأخر البحث عن التطبيع. تعتمد أساليب تطبيع Sota الحالية على قواعد أو قواعد النحوية التي صممها الخبراء، مما يحد من أدائها في شريوس الناشئة، مثل نصوص وسائل التواصل الاجتماعي. في هذه الورقة، نقوم بالنماذج تعبير التطبيع في الوقت نفسه كتسلسل للعمليات لبناء القيمة الزمنية الطبيعية، ونقدم طريقة جديدة تسمى Artime، والتي يمكن أن تولد تلقائيا قواعد التطبيع من بيانات التدريب دون تدخلات خبراء. على وجه التحديد، يلتقط Artime تلقائيا تسلسل عمليات التشغيل الممكنة من البيانات المشروحة ويولد قواعد التطبيع في التعبيرات الزمنية بأشكال سطح مشتركة. تظهر النتائج التجريبية أن Artime يمكن أن يتجاوز بشكل كبير أساليب Sota على القياس التغريدات، وتحقق نتائج تنافسية مع طرق القاعدة التي تم تصميمها من الخبراء الموجودة في معيار Tempeval-3.
The understanding of time expressions includes two sub-tasks: recognition and normalization. In recent years, significant progress has been made in the recognition of time expressions while research on normalization has lagged behind. Existing SOTA normalization methods highly rely on rules or grammars designed by experts, which limits their performance on emerging corpora, such as social media texts. In this paper, we model time expression normalization as a sequence of operations to construct the normalized temporal value, and we present a novel method called ARTime, which can automatically generate normalization rules from training data without expert interventions. Specifically, ARTime automatically captures possible operation sequences from annotated data and generates normalization rules on time expressions with common surface forms. The experimental results show that ARTime can significantly surpass SOTA methods on the Tweets benchmark, and achieves competitive results with existing expert-engineered rule methods on the TempEval-3 benchmark.
References used
https://aclanthology.org/
rate research
Read More
The shift to neural models in Referring Expression Generation (REG) has enabled more natural set-ups, but at the cost of interpretability. We argue that integrating pragmatic reasoning into the inference of context-agnostic generation models could re
concile traits of traditional and neural REG, as this offers a separation between context-independent, literal information and pragmatic adaptation to context. With this in mind, we apply existing decoding strategies from discriminative image captioning to REG and evaluate them in terms of pragmatic informativity, likelihood to ground-truth annotations and linguistic diversity. Our results show general effectiveness, but a relatively small gain in informativity, raising important questions for REG in general.
Automated storytelling has long captured the attention of researchers for the ubiquity of narratives in everyday life. The best human-crafted stories exhibit coherent plot, strong characters, and adherence to genres, attributes that current states-of
-the-art still struggle to produce, even using transformer architectures. In this paper, we analyze works in story generation that utilize machine learning approaches to (1) address story generation controllability, (2) incorporate commonsense knowledge, (3) infer reasonable character actions, and (4) generate creative language.
We focus on a type of linguistic formal reasoning where the goal is to reason over explicit knowledge in the form of natural language facts and rules (Clark et al., 2020). A recent work, named PRover (Saha et al., 2020), performs such reasoning by an
swering a question and also generating a proof graph that explains the answer. However, compositional reasoning is not always unique and there may be multiple ways of reaching the correct answer. Thus, in our work, we address a new and challenging problem of generating multiple proof graphs for reasoning over natural language rule-bases. Each proof provides a different rationale for the answer, thereby improving the interpretability of such reasoning systems. In order to jointly learn from all proof graphs and exploit the correlations between multiple proofs for a question, we pose this task as a set generation problem over structured output spaces where each proof is represented as a directed graph. We propose two variants of a proof-set generation model, multiPRover. Our first model, Multilabel-multiPRover, generates a set of proofs via multi-label classification and implicit conditioning between the proofs; while the second model, Iterative-multiPRover, generates proofs iteratively by explicitly conditioning on the previously generated proofs. Experiments on multiple synthetic, zero-shot, and human-paraphrased datasets reveal that both multiPRover models significantly outperform PRover on datasets containing multiple gold proofs. Iterative-multiPRover obtains state-of-the-art proof F1 in zero-shot scenarios where all examples have single correct proofs. It also generalizes better to questions requiring higher depths of reasoning where multiple proofs are more frequent.
Online misinformation is a prevalent societal issue, with adversaries relying on tools ranging from cheap fakes to sophisticated deep fakes. We are motivated by the threat scenario where an image is used out of context to support a certain narrative.
While some prior datasets for detecting image-text inconsistency generate samples via text manipulation, we propose a dataset where both image and text are unmanipulated but mismatched. We introduce several strategies for automatically retrieving convincing images for a given caption, capturing cases with inconsistent entities or semantic context. Our large-scale automatically generated the NewsCLIPpings Dataset: (1) demonstrates that machine-driven image repurposing is now a realistic threat, and (2) provides samples that represent challenging instances of mismatch between text and image in news that are able to mislead humans. We benchmark several state-of-the-art multimodal models on our dataset and analyze their performance across different pretraining domains and visual backbones.
This paper tries to examine the relationship between analogy and the grammatical
rule. Analogy is one of the basic principles and bases of Arabic grammar during times of
rule formation and judging it. Linguists were divided in their attitude to ana
logy, with
some supporting it and others against it. Grammarians were more inclined toward analogy
than compilers, because grammarians’ research was based on the existing similarity
between words, phrases, and style used in speech reported by tellers of what had been said
by the Arabs. They based their rules and origins of analogy on that similarity. Analogists
transliterated some foreign terms, Arabized, and derived new words out of them in a
manner similar to that done with Arabic terms. However, some grammarians went very far
in their excessive use of analogy to the extent that it becomes far removed from linguistic
reality to be a form riddle and guessing, leading to reaction against analogy then against
grammar. Analogy became an end in itself; it overlooked its original purpose; was then
manifested in rule formation of words said spontaneously.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
