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Register a new userBreeding Fillmore's Chickens and Hatching the Eggs: Recombining Frames and Roles in Frame-Semantic Parsing
تربية الدجاج Fillmore وتفريخ البيض: إطارات إعادة التدوير والأدوار في تحليل الإطار الدلالي
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المحللون الدلالي الإطار يتوقعون تقليديا التنبؤات والأطر والأدوار الدلالية في ترتيب ثابت.تستكشف هذه الورقة مشكلة الدجاج أو البيض للمشكلة بين هذه المكونات نظريا ومن الناحية النظرية.نقدم بنية تسلسل تسلسل تستند إلى بيرت مرنة تسمح بتنبؤ الإطارات والأدوار بشكل مستقل عن بعضها البعض أو الجمع بينها بعدة طرق.تبين نتائجنا أن الإعدادات لدينا يمكن أن تقريب أداء النماذج التقليدية أكثر تعقيدا، مع السماح بمنظر أكثر وضوحا للترابط بين مكونات خط الأنابيب، وكيف تجعل نماذج التنبؤ بالأطر والأدب استخدام طبقات بيرت مختلفة.
Frame-semantic parsers traditionally predict predicates, frames, and semantic roles in a fixed order. This paper explores the chicken-or-egg' problem of interdependencies between these components theoretically and practically. We introduce a flexible BERT-based sequence labeling architecture that allows for predicting frames and roles independently from each other or combining them in several ways. Our results show that our setups can approximate more complex traditional models' performance, while allowing for a clearer view of the interdependencies between the pipeline's components, and of how frame and role prediction models make different use of BERT's layers.
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This paper introduces Semantic Frame Forecast, a task that predicts the semantic frames that will occur in the next 10, 100, or even 1,000 sentences in a running story. Prior work focused on predicting the immediate future of a story, such as one to
a few sentences ahead. However, when novelists write long stories, generating a few sentences is not enough to help them gain high-level insight to develop the follow-up story. In this paper, we formulate a long story as a sequence of story blocks,'' where each block contains a fixed number of sentences (e.g., 10, 100, or 200). This formulation allows us to predict the follow-up story arc beyond the scope of a few sentences. We represent a story block using the term frequencies (TF) of semantic frames in it, normalized by each frame's inverse document frequency (IDF). We conduct semantic frame forecast experiments on 4,794 books from the Bookcorpus and 7,962 scientific abstracts from CODA-19, with block sizes ranging from 5 to 1,000 sentences. The results show that automated models can forecast the follow-up story blocks better than the random, prior, and replay baselines, indicating the feasibility of the task. We also learn that the models using the frame representation as features outperform all the existing approaches when the block size is over 150 sentences. The human evaluation also shows that the proposed frame representation, when visualized as word clouds, is comprehensible, representative, and specific to humans.
Frame semantic parsing is a semantic analysis task based on FrameNet which has received great attention recently. The task usually involves three subtasks sequentially: (1) target identification, (2) frame classification and (3) semantic role labelin
g. The three subtasks are closely related while previous studies model them individually, which ignores their intern connections and meanwhile induces error propagation problem. In this work, we propose an end-to-end neural model to tackle the task jointly. Concretely, we exploit a graph-based method, regarding frame semantic parsing as a graph construction problem. All predicates and roles are treated as graph nodes, and their relations are taken as graph edges. Experiment results on two benchmark datasets of frame semantic parsing show that our method is highly competitive, resulting in better performance than pipeline models.
Sentence-level extractive text summarization aims to select important sentences from a given document. However, it is very challenging to model the importance of sentences. In this paper, we propose a novel Frame Semantic-Enhanced Sentence Modeling f
or Extractive Summarization, which leverages Frame semantics to model sentences from both intra-sentence level and inter-sentence level, facilitating the text summarization task. In particular, intra-sentence level semantics leverage Frames and Frame Elements to model internal semantic structure within a sentence, while inter-sentence level semantics leverage Frame-to-Frame relations to model relationships among sentences. Extensive experiments on two benchmark corpus CNN/DM and NYT demonstrate that our model outperforms six state-of-the-art methods significantly.
Despite recent advances in semantic role labeling propelled by pre-trained text encoders like BERT, performance lags behind when applied to predicates observed infrequently during training or to sentences in new domains. In this work, we investigate
how role labeling performance on low-frequency predicates and out-of-domain data can be further improved by using VerbNet, a verb lexicon that groups verbs into hierarchical classes based on shared syntactic and semantic behavior and defines semantic representations describing relations between arguments. We find that VerbNet classes provide an effective level of abstraction, improving generalization on low-frequency predicates by allowing them to learn from the training examples of other predicates belonging to the same class. We also find that joint training of VerbNet role labeling and predicate disambiguation of VerbNet classes for polysemous verbs leads to improvements in both tasks, naturally supporting the extraction of VerbNet's semantic representations.
Two experiments were conducted to evaluate the effect of ascorbic acid
(AA) during egg incubation on development of broiler chickens of a
commercial stock. In Experiment ١, eggs with living embryos were injected at
١٥ d of incubation with ٠٫١ ml o
f saline solution containing either ٣ or ١٢ mg of
AA per egg and uninjected control. Body weights of each treatment were
determined weekly from hatch to ٦ wk of age. In Experiment ٢, the treatments
at ١٥ d of incubation were as follows: ١) eggs injected with ٣ mg of AA and
then cooled at ٢٢° C for ٢٤ h; ٢) eggs dipped in ٣٪ solution of AA for ٣ min
and then cooled at ٢٢° C for ٢٤ h; and ٣) control. Embryo weight at ١٩ d of
incubation, hatchability and body weights of the hatched chicks were
determined.
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