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Register a new userZero-Shot Cross-Lingual Transfer is a Hard Baseline to Beat in German Fine-Grained Entity Typing
التحويل الصفر - اللطاحي هو خط أساس صلب للتغلب على الكتابة الكيانية الجميلة الألمانية
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غالبا ما يتطلب تدريب نماذج NLP كميات كبيرة من بيانات التدريب المسمى، مما يجعل من الصعب توسيع النماذج الحالية لغات جديدة.في حين تعتمد Transfer-Transfer عبر اللغات الصفرية على تضييق كلمة متعددة اللغات لتطبيق نموذج تدرب على لغة واحدة لآخر، فإن Yarowski و Ngai (2001) يقترح طريقة الإسقاط التوضيحي لتوليد بيانات التدريب دون شرح يدوي.تم استخدام هذه الطريقة بنجاح مهام التعرف على الكيان المسمى وكتابة الكيان الخشن الخشبي، لكننا نظهر أنه من غير متوقع من قبل النقل الصفرية عبر اللغات عند تطبيقها على مهمة مماثلة لكتابة الكيان المحبوس.في دراستنا لطبقتها الجميلة للكتابة من نوع الغش في علم الأطباق الألمانية بالنسبة للألمانية، نظهر أن الإسقاط التوضيحي يضخم ميل النموذج الإنجليزي إلى تسميات المستوى 2 المستويات والضرب عن طريق النقل الصفرية عبر اللغات على ثلاثة مجموعات اختبار رواية.
The training of NLP models often requires large amounts of labelled training data, which makes it difficult to expand existing models to new languages. While zero-shot cross-lingual transfer relies on multilingual word embeddings to apply a model trained on one language to another, Yarowski and Ngai (2001) propose the method of annotation projection to generate training data without manual annotation. This method was successfully used for the tasks of named entity recognition and coarse-grained entity typing, but we show that it is outperformed by zero-shot cross-lingual transfer when applied to the similar task of fine-grained entity typing. In our study of fine-grained entity typing with the FIGER type ontology for German, we show that annotation projection amplifies the English model's tendency to underpredict level 2 labels and is beaten by zero-shot cross-lingual transfer on three novel test sets.
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Analyzing microblogs where we post what we experience enables us to perform various applications such as social-trend analysis and entity recommendation. To track emerging trends in a variety of areas, we want to categorize information on emerging en
tities (e.g., Avatar 2) in microblog posts according to their types (e.g., Film). We thus introduce a new entity typing task that assigns a fine-grained type to each emerging entity when a burst of posts containing that entity is first observed in a microblog. The challenge is to perform typing from noisy microblog posts without relying on prior knowledge of the target entity. To tackle this task, we build large-scale Twitter datasets for English and Japanese using time-sensitive distant supervision. We then propose a modular neural typing model that encodes not only the entity and its contexts but also meta information in multiple posts. To type homographic' emerging entities (e.g., Go' means an emerging programming language and a classic board game), which contexts are noisy, we devise a context selector that finds related contexts of the target entity. Experiments on the Twitter datasets confirm the effectiveness of our typing model and the context selector.
Conventional entity typing approaches are based on independent classification paradigms, which make them difficult to recognize inter-dependent, long-tailed and fine-grained entity types. In this paper, we argue that the implicitly entailed extrinsic
and intrinsic dependencies between labels can provide critical knowledge to tackle the above challenges. To this end, we propose Label Reasoning Network(LRN), which sequentially reasons fine-grained entity labels by discovering and exploiting label dependencies knowledge entailed in the data. Specifically, LRN utilizes an auto-regressive network to conduct deductive reasoning and a bipartite attribute graph to conduct inductive reasoning between labels, which can effectively model, learn and reason complex label dependencies in a sequence-to-set, end-to-end manner. Experiments show that LRN achieves the state-of-the-art performance on standard ultra fine-grained entity typing benchmarks, and can also resolve the long tail label problem effectively.
Existing work on Fine-grained Entity Typing (FET) typically trains automatic models on the datasets obtained by using Knowledge Bases (KB) as distant supervision. However, the reliance on KB means this training setting can be hampered by the lack of
or the incompleteness of the KB. To alleviate this limitation, we propose a novel setting for training FET models: FET without accessing any knowledge base. Under this setting, we propose a two-step framework to train FET models. In the first step, we automatically create pseudo data with fine-grained labels from a large unlabeled dataset. Then a neural network model is trained based on the pseudo data, either in an unsupervised way or using self-training under the weak guidance from a coarse-grained Named Entity Recognition (NER) model. Experimental results show that our method achieves competitive performance with respect to the models trained on the original KB-supervised datasets.
Multilingual pre-trained models have achieved remarkable performance on cross-lingual transfer learning. Some multilingual models such as mBERT, have been pre-trained on unlabeled corpora, therefore the embeddings of different languages in the models
may not be aligned very well. In this paper, we aim to improve the zero-shot cross-lingual transfer performance by proposing a pre-training task named Word-Exchange Aligning Model (WEAM), which uses the statistical alignment information as the prior knowledge to guide cross-lingual word prediction. We evaluate our model on multilingual machine reading comprehension task MLQA and natural language interface task XNLI. The results show that WEAM can significantly improve the zero-shot performance.
In cross-lingual Abstract Meaning Representation (AMR) parsing, researchers develop models that project sentences from various languages onto their AMRs to capture their essential semantic structures: given a sentence in any language, we aim to captu
re its core semantic content through concepts connected by manifold types of semantic relations. Methods typically leverage large silver training data to learn a single model that is able to project non-English sentences to AMRs. However, we find that a simple baseline tends to be overlooked: translating the sentences to English and projecting their AMR with a monolingual AMR parser (translate+parse,T+P). In this paper, we revisit this simple two-step base-line, and enhance it with a strong NMT system and a strong AMR parser. Our experiments show that T+P outperforms a recent state-of-the-art system across all tested languages: German, Italian, Spanish and Mandarin with +14.6, +12.6, +14.3 and +16.0 Smatch points
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