Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userproScript: Partially Ordered Scripts Generation
محادثة: أمرت جزئيا توليد البرامج النصية
99
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
البرامج النصية - تسلسل الأحداث النموذجية التي تصف الأنشطة اليومية - تساعد في فهم الروايات من خلال توفير التوقعات، وحل الغموض، وملء المعلومات غير المستحقة. ومع ذلك، حتى الآن أثبتوا صعوبة في المؤلف أو استخراج النص. في هذا العمل، نوضح لأول مرة يمكن تصوير نماذج اللغات العصبية المدربة مسبقا لتوليد البرامج النصية عالية الجودة، في مستويات مختلفة من الحبيبية، لمجموعة واسعة من السيناريوهات اليومية (E.G.، خبز كعكة). للقيام بذلك، نقوم بجمع Growdsourced كبيرة (6.4K) من البرامج النصية التي أمرت جزئيا (المسمى المحترفات)، وهي أكبر بكثير من مجموعات البيانات السابقة، وتطوير النماذج التي تولد البرامج النصية من خلال الجمع بين توليد اللغة والتنبؤ بنية الرسم البياني. نحدد اثنين من المهام التكميلية: (1) التنبؤ الحافة: بالنظر إلى السيناريو والأحداث غير المدرجة، قم بتنظيم الأحداث في البرنامج النصي ساري المفعول (ربما بالترتيب الجزئي)، و (2) جيل البرنامج النصي: معطى سيناريو فقط، توليد الأحداث وتنظيمها في البرنامج النصي (ربما النظام الجزئي). تظهر تجاربنا أن نماذجنا تؤدي جيدا (على سبيل المثال، F1 = 75.7 في المهمة (1))، مما يوضح نهجا جديدا للتغلب على الحواجز السابقة أمام جمع البرنامج النصي. نظهر أيضا أنه لا يزال هناك مجال مهم للتحسين نحو أداء مستوى الإنسان. معا، توفر المهام الخاصة بنا ومجموعة البيانات والنماذج اتجاها بحثا جديدا لتعلم معرفة البرنامج النصي.
Scripts -- prototypical event sequences describing everyday activities -- have been shown to help understand narratives by providing expectations, resolving ambiguity, and filling in unstated information. However, to date they have proved hard to author or extract from text. In this work, we demonstrate for the first time that pre-trained neural language models can be finetuned to generate high-quality scripts, at varying levels of granularity, for a wide range of everyday scenarios (e.g., bake a cake). To do this, we collect a large (6.4k) crowdsourced partially ordered scripts (named proScript), that is substantially larger than prior datasets, and develop models that generate scripts by combining language generation and graph structure prediction. We define two complementary tasks: (i) edge prediction: given a scenario and unordered events, organize the events into a valid (possibly partial-order) script, and (ii) script generation: given only a scenario, generate events and organize them into a (possibly partial-order) script. Our experiments show that our models perform well (e.g., F1=75.7 on task (i)), illustrating a new approach to overcoming previous barriers to script collection. We also show that there is still significant room for improvement toward human level performance. Together, our tasks, dataset, and models offer a new research direction for learning script knowledge.
References used
https://aclanthology.org/
rate research
Read More
Most undeciphered lost languages exhibit two characteristics that pose significant decipherment challenges: (1) the scripts are not fully segmented into words; (2) the closest known language is not determined. We propose a decipherment model that han
dles both of these challenges by building on rich linguistic constraints reflecting consistent patterns in historical sound change. We capture the natural phonological geometry by learning character embeddings based on the International Phonetic Alphabet (IPA). The resulting generative framework jointly models word segmentation and cognate alignment, informed by phonological constraints. We evaluate the model on both deciphered languages (Gothic, Ugaritic) and an undeciphered one (Iberian). The experiments show that incorporating phonetic geometry leads to clear and consistent gains. Additionally, we propose a measure for language closeness which correctly identifies related languages for Gothic and Ugaritic. For Iberian, the method does not show strong evidence supporting Basque as a related language, concurring with the favored position by the current scholarship.1
In this paper, we introduce the task of predicting severity of age-restricted aspects of movie content based solely on the dialogue script. We first investigate categorizing the ordinal severity of movies on 5 aspects: Sex, Violence, Profanity, Subst
ance consumption, and Frightening scenes. The problem is handled using a siamese network-based multitask framework which concurrently improves the interpretability of the predictions. The experimental results show that our method outperforms the previous state-of-the-art model and provides useful information to interpret model predictions. The proposed dataset and source code are publicly available at our GitHub repository.
Generating informative and appropriate responses is challenging but important for building human-like dialogue systems. Although various knowledge-grounded conversation models have been proposed, these models have limitations in utilizing knowledge t
hat infrequently occurs in the training data, not to mention integrating unseen knowledge into conversation generation. In this paper, we propose an Entity-Agnostic Representation Learning (EARL) method to introduce knowledge graphs to informative conversation generation. Unlike traditional approaches that parameterize the specific representation for each entity, EARL utilizes the context of conversations and the relational structure of knowledge graphs to learn the category representation for entities, which is generalized to incorporating unseen entities in knowledge graphs into conversation generation. Automatic and manual evaluations demonstrate that our model can generate more informative, coherent, and natural responses than baseline models.
For updating the translations of Japanese statutes based on their amendments, we need to consider the translation focality;'' that is, we should only modify expressions that are relevant to the amendment and retain the others to avoid misconstruing i
ts contents. In this paper, we introduce an evaluation metric and a corpus to improve focality evaluations. Our metric is called an Inclusive Score for DIfferential Translation: (ISDIT). ISDIT consists of two factors: (1) the n-gram recall of expressions unaffected by the amendment and (2) the n-gram precision of the output compared to the reference. This metric supersedes an existing one for focality by simultaneously calculating the translation quality of the changed expressions in addition to that of the unchanged expressions. We also newly compile a corpus for Japanese partially amendment translation that secures the focality of the post-amendment translations, while an existing evaluation corpus does not. With the metric and the corpus, we examine the performance of existing translation methods for Japanese partially amendment translations.
Abstract We study learning named entity recognizers in the presence of missing entity annotations. We approach this setting as tagging with latent variables and propose a novel loss, the Expected Entity Ratio, to learn models in the presence of syste
matically missing tags. We show that our approach is both theoretically sound and empirically useful. Experimentally, we find that it meets or exceeds performance of strong and state-of-the-art baselines across a variety of languages, annotation scenarios, and amounts of labeled data. In particular, we find that it significantly outperforms the previous state-of-the-art methods from Mayhew et al. (2019) and Li et al. (2021) by +12.7 and +2.3 F1 score in a challenging setting with only 1,000 biased annotations, averaged across 7 datasets. We also show that, when combined with our approach, a novel sparse annotation scheme outperforms exhaustive annotation for modest annotation budgets.1
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
