Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userModeling Document-Level Context for Event Detection via Important Context Selection
النمذجة السياق على مستوى المستند للكشف عن الحدث من خلال اختيار السياق الهام
382
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تهدف مهمة اكتشاف الحدث (ED) في استخراج المعلومات إلى الاعتراف وتصنيف كلمات الأحداث في النص. تميز التقدم الأخير نماذج لغوية متقدمة للمحولات المتقدمة (على سبيل المثال، بيرت) كعنصر حاسم في النماذج الحديثة للإد. ومع ذلك، فإن الحد الطول لنصوص الإدخال هو حاجز لمثل هذه النماذج المحددة لأنها لا تستطيع تشفير سياق مستوي المستند طويل المدى الذي ثبت أنه مفيد لإد إد. لمعالجة هذه المشكلة، نقترح طريقة رواية لنموذج سياق مستوى المستندات لتحديد الجمل ذات الصلة بشكل حيوي في وثيقة التنبؤ بالحدث بالسجن الهدف. سيتم بعد ذلك زيادة الجملة المستهدفة بالجمل المختارة وتستهلكها النماذج اللغوية القائمة على المحولات لتعلم التمثيل المحسن. تحقيقا لهذه الغاية، يتم استخدام خوارزمية التعزيز لتدريب اختيار الجملة ذات الصلة من أجل إد. يتم بعد ذلك تقديم العديد من أنواع المعلومات لتشكيل وظيفة المكافآت لعملية التدريب، بما في ذلك أداء إد، وإشراك الجملة، وعلاقات الخطاب. تجاه تجاربنا الواسعة على مجموعات البيانات القياسية المتعددة تكشف عن فعالية النموذج المقترح، مما يؤدي إلى أداء جديد من الفنادق الجديدة.
The task of Event Detection (ED) in Information Extraction aims to recognize and classify trigger words of events in text. The recent progress has featured advanced transformer-based language models (e.g., BERT) as a critical component in state-of-the-art models for ED. However, the length limit for input texts is a barrier for such ED models as they cannot encode long-range document-level context that has been shown to be beneficial for ED. To address this issue, we propose a novel method to model document-level context for ED that dynamically selects relevant sentences in the document for the event prediction of the target sentence. The target sentence will be then augmented with the selected sentences and consumed entirely by transformer-based language models for improved representation learning for ED. To this end, the REINFORCE algorithm is employed to train the relevant sentence selection for ED. Several information types are then introduced to form the reward function for the training process, including ED performance, sentence similarity, and discourse relations. Our extensive experiments on multiple benchmark datasets reveal the effectiveness of the proposed model, leading to new state-of-the-art performance.
References used
https://aclanthology.org/
rate research
Read More
Natural language generation (NLG) tasks on pro-drop languages are known to suffer from zero pronoun (ZP) problems, and the problems remain challenging due to the scarcity of ZP-annotated NLG corpora. In this case, we propose a highly adaptive two-sta
ge approach to couple context modeling with ZP recovering to mitigate the ZP problem in NLG tasks. Notably, we frame the recovery process in a task-supervised fashion where the ZP representation recovering capability is learned during the NLG task learning process, thus our method does not require NLG corpora annotated with ZPs. For system enhancement, we learn an adversarial bot to adjust our model outputs to alleviate the error propagation caused by mis-recovered ZPs. Experiments on three document-level NLG tasks, i.e., machine translation, question answering, and summarization, show that our approach can improve the performance to a great extent, and the improvement on pronoun translation is very impressive.
Implicit discourse relation recognition (IDRR) aims to identify logical relations between two adjacent sentences in the discourse. Existing models fail to fully utilize the contextual information which plays an important role in interpreting each loc
al sentence. In this paper, we thus propose a novel graph-based Context Tracking Network (CT-Net) to model the discourse context for IDRR. The CT-Net firstly converts the discourse into the paragraph association graph (PAG), where each sentence tracks their closely related context from the intricate discourse through different types of edges. Then, the CT-Net extracts contextual representation from the PAG through a specially designed cross-grained updating mechanism, which can effectively integrate both sentence-level and token-level contextual semantics. Experiments on PDTB 2.0 show that the CT-Net gains better performance than models that roughly model the context.
Document machine translation aims to translate the source sentence into the target language in the presence of additional contextual information. However, it typically suffers from a lack of doc-level bilingual data. To remedy this, here we propose a
simple yet effective context-interactive pre-training approach, which targets benefiting from external large-scale corpora. The proposed model performs inter sentence generation to capture the cross-sentence dependency within the target document, and cross sentence translation to make better use of valuable contextual information. Comprehensive experiments illustrate that our approach can achieve state-of-the-art performance on three benchmark datasets, which significantly outperforms a variety of baselines.
Embedding based methods are widely used for unsupervised keyphrase extraction (UKE) tasks. Generally, these methods simply calculate similarities between phrase embeddings and document embedding, which is insufficient to capture different context for
a more effective UKE model. In this paper, we propose a novel method for UKE, where local and global contexts are jointly modeled. From a global view, we calculate the similarity between a certain phrase and the whole document in the vector space as transitional embedding based models do. In terms of the local view, we first build a graph structure based on the document where phrases are regarded as vertices and the edges are similarities between vertices. Then, we proposed a new centrality computation method to capture local salient information based on the graph structure. Finally, we further combine the modeling of global and local context for ranking. We evaluate our models on three public benchmarks (Inspec, DUC 2001, SemEval 2010) and compare with existing state-of-the-art models. The results show that our model outperforms most models while generalizing better on input documents with different domains and length. Additional ablation study shows that both the local and global information is crucial for unsupervised keyphrase extraction tasks.
Since their inception, transformer-based language models have led to impressive performance gains across multiple natural language processing tasks. For Arabic, the current state-of-the-art results on most datasets are achieved by the AraBERT languag
e model. Notwithstanding these recent advancements, sarcasm and sentiment detection persist to be challenging tasks in Arabic, given the language's rich morphology, linguistic disparity and dialectal variations. This paper proffers team SPPU-AASM's submission for the WANLP ArSarcasm shared-task 2021, which centers around the sarcasm and sentiment polarity detection of Arabic tweets. The study proposes a hybrid model, combining sentence representations from AraBERT with static word vectors trained on Arabic social media corpora. The proposed system achieves a F1-sarcastic score of 0.62 and a F-PN score of 0.715 for the sarcasm and sentiment detection tasks, respectively. Simulation results show that the proposed system outperforms multiple existing approaches for both the tasks, suggesting that the amalgamation of context-free and context-dependent text representations can help capture complementary facets of word meaning in Arabic. The system ranked second and tenth in the respective sub-tasks of sarcasm detection and sentiment identification.
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
