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Register a new userConundrums in Event Coreference Resolution: Making Sense of the State of the Art
الدلائل في حفل تحليل القرار: إحساس بحالة الفن
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على الرغم من النتائج الواعدة الأخيرة بشأن تطبيق النماذج المستندة إلى التفسير المرجعي للحدث، هناك نقص في فهم ما تم تحسينه.نقدم تحليلا تجريبيا للنظم المرجعية في الأحداث المصنعة للدولة من بين الفنون، بهدف توفير جمهور NLP العام بفهم أفضل لحالة الباحثين والباحثين المرجعيين باتجاهات البحث في المستقبل.
Despite recent promising results on the application of span-based models for event reference interpretation, there is a lack of understanding of what has been improved. We present an empirical analysis of a state-of-the-art span-based event reference systems with the goal of providing the general NLP audience with a better understanding of the state of the art and reference researchers with directions for future research.
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While Yu and Poesio (2020) have recently demonstrated the superiority of their neural multi-task learning (MTL) model to rule-based approaches for bridging anaphora resolution, there is little understanding of (1) how it is better than the rule-based
approaches (e.g., are the two approaches making similar or complementary mistakes?) and (2) what should be improved. To shed light on these issues, we (1) propose a hybrid rule-based and MTL approach that would enable a better understanding of their comparative strengths and weaknesses; and (2) perform a manual analysis of the errors made by the MTL model.
Coreference resolution is key to many natural language processing tasks and yet has been relatively unexplored in Sign Language Processing. In signed languages, space is primarily used to establish reference. Solving coreference resolution for signed
languages would not only enable higher-level Sign Language Processing systems, but also enhance our understanding of language in different modalities and of situated references, which are key problems in studying grounded language. In this paper, we: (1) introduce Signed Coreference Resolution (SCR), a new challenge for coreference modeling and Sign Language Processing; (2) collect an annotated corpus of German Sign Language with gold labels for coreference together with an annotation software for the task; (3) explore features of hand gesture, iconicity, and spatial situated properties and move forward to propose a set of linguistically informed heuristics and unsupervised models for the task; (4) put forward several proposals about ways to address the complexities of this challenge effectively.
This paper focuses on paraphrase generation,which is a widely studied natural language generation task in NLP. With the development of neural models, paraphrase generation research has exhibited a gradual shift to neural methods in the recent years.
This has provided architectures for contextualized representation of an input text and generating fluent, diverseand human-like paraphrases. This paper surveys various approaches to paraphrase generation with a main focus on neural methods.
The ancient civilization of Mesopotamia expressed widely its high levels of political and civilized development at that time. That progress reflected on the evolution of all aspects of life, especially on the artistic one. Since ancient times, Mural
Art got an obvious role in bringing religion closer to the minds of people through decorating temples and taking care of its ornamentation and graphics.
Mesopotamia was distinguished for its prodigious artistic production, which was well known by its precision in expressing various topics. The Assyrians made a revolution in the history of Murals art by suggesting depth within the painting itself. The Assyrian art was also superior in showing the physical strength in the human, animal and predators' forms. It also focused on documenting the of glory of heroism and military victories.
We propose a neural event coreference model in which event coreference is jointly trained with five tasks: trigger detection, entity coreference, anaphoricity determination, realis detection, and argument extraction. To guide the learning of this com
plex model, we incorporate cross-task consistency constraints into the learning process as soft constraints via designing penalty functions. In addition, we propose the novel idea of viewing entity coreference and event coreference as a single coreference task, which we believe is a step towards a unified model of coreference resolution. The resulting model achieves state-of-the-art results on the KBP 2017 event coreference dataset.
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