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Register a new userAttentive Tree-structured Network for Monotonicity Reasoning
شبكة منظم شجرة اليقظة للمنطق الرتامي
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العديد من النماذج العصبية الحديثة المصممة للمناسبة الروماتونية تؤدي بشكل سيء على الاستدلال النزولي.لمعالجة هذا القصور، قمنا بتطوير شبكة عصبية منظم بشجرة اليقظة.وهي تتألف من شبكة ذاكرة طويلة الأجل على المدى القصير (TREE-LSTM) مع اهتمام ناعم.تم تصميمه لنموذج معلومات شجرة التحليل النحوية من زوج الجملة من مهمة التفكير.يتم استخدام مجمع ذاتي اليقظة لمحاذاة تمثيلات الفرضية والفرضية.نقدم نموذجنا وتقييمه باستخدام DataSet EnteraTment Monoticity (MED).نعرض ونحاول شرح أن نموذجنا يفوق النماذج الحالية على ميد.
Many state-of-art neural models designed for monotonicity reasoning perform poorly on downward inference. To address this shortcoming, we developed an attentive tree-structured neural network. It consists of a tree-based long-short-term-memory network (Tree-LSTM) with soft attention. It is designed to model the syntactic parse tree information from the sentence pair of a reasoning task. A self-attentive aggregator is used for aligning the representations of the premise and the hypothesis. We present our model and evaluate it using the Monotonicity Entailment Dataset (MED). We show and attempt to explain that our model outperforms existing models on MED.
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Growing interests have been attracted in Conversational Recommender Systems (CRS), which explore user preference through conversational interactions in order to make appropriate recommendation. However, there is still a lack of ability in existing CR
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Recent QA with logical reasoning questions requires passage-level relations among the sentences. However, current approaches still focus on sentence-level relations interacting among tokens. In this work, we explore aggregating passage-level clues fo
r solving logical reasoning QA by using discourse-based information. We propose a discourse-aware graph network (DAGN) that reasons relying on the discourse structure of the texts. The model encodes discourse information as a graph with elementary discourse units (EDUs) and discourse relations, and learns the discourse-aware features via a graph network for downstream QA tasks. Experiments are conducted on two logical reasoning QA datasets, ReClor and LogiQA, and our proposed DAGN achieves competitive results. The source code is available at https://github.com/Eleanor-H/DAGN.
Abstract Although current CCG supertaggers achieve high accuracy on the standard WSJ test set, few systems make use of the categories' internal structure that will drive the syntactic derivation during parsing. The tagset is traditionally truncated,
discarding the many rare and complex category types in the long tail. However, supertags are themselves trees. Rather than give up on rare tags, we investigate constructive models that account for their internal structure, including novel methods for tree-structured prediction. Our best tagger is capable of recovering a sizeable fraction of the long-tail supertags and even generates CCG categories that have never been seen in training, while approximating the prior state of the art in overall tag accuracy with fewer parameters. We further investigate how well different approaches generalize to out-of-domain evaluation sets.
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Math word problem solving has attracted considerable research interest in recent years. Previous works have shown the effectiveness of utilizing graph neural networks to capture the relationships in the problem. However, these works did not carefully
take the edge label information and the long-range word relationship across sentences into consideration. In addition, during generation, they focus on the most relevant areas of the currently generated word, while neglecting the rest of the problem. In this paper, we propose a novel Edge-Enhanced Hierarchical Graph-to-Tree model (EEH-G2T), in which the math word problems are represented as edge-labeled graphs. Specifically, an edge-enhanced hierarchical graph encoder is used to incorporate edge label information. This encoder updates the graph nodes hierarchically in two steps: sentence-level aggregation and problem-level aggregation. Furthermore, a tree-structured decoder with a split attention mechanism is applied to guide the model to pay attention to different parts of the input problem. Experimental results on the MAWPS and Math23K dataset showed that our EEH-G2T can effectively improve performance compared with state-of-the-art methods.
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