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Register a new userUnderstanding Politics via Contextualized Discourse Processing
فهم السياسة عن طريق معالجة الخطاب السياقي
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غالبا ما يكون لدى السياسيين جداول أعمال أساسية عند الرد على الأحداث.تعكس الحجج في سياقات الأحداث المختلفة مجموعة متسقة إلى حد ما من جدول أعمال كيان معين.على الرغم من التطورات الأخيرة في نماذج اللغة المحددة مسبقا، فإن هذه التمثيلات النصية غير مصممة لالتقاط مثل هذه الأنماط الدقيقة.في هذه الورقة، نقترح نموذج قارئ ترخيص يتكون من وحدات التشفير والملحن، والتي تلتقط هذه المعلومات وتزود هذه المعلومات لتوليد تمثيلات أكثر فعالية للكيانات والقضايا والأحداث.هذه التمثيلات محكوم علي تغريدات، البيانات الصحفية، والقضايا، والمقالات الإخبارية، والكيانات المشاركة.يعالج النموذج لدينا العديد من المستندات في وقت واحد ويولد تمثيلات مؤلفة للحصول على كيانات متعددة على العديد من القضايا أو الأحداث.من خلال التحليل التجريبي النوعي والكمي، نوضح أن هذه التمثيلات ذات مغزى وفعال.
Politicians often have underlying agendas when reacting to events. Arguments in contexts of various events reflect a fairly consistent set of agendas for a given entity. In spite of recent advances in Pretrained Language Models, those text representations are not designed to capture such nuanced patterns. In this paper, we propose a Compositional Reader model consisting of encoder and composer modules, that captures and leverages such information to generate more effective representations for entities, issues, and events. These representations are contextualized by tweets, press releases, issues, news articles, and participating entities. Our model processes several documents at once and generates composed representations for multiple entities over several issues or events. Via qualitative and quantitative empirical analysis, we show that these representations are meaningful and effective.
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In this paper, we propose a definition and taxonomy of various types of non-standard textual content -- generally referred to as noise'' -- in Natural Language Processing (NLP). While data pre-processing is undoubtedly important in NLP, especially wh
en dealing with user-generated content, a broader understanding of different sources of noise and how to deal with them is an aspect that has been largely neglected. We provide a comprehensive list of potential sources of noise, categorise and describe them, and show the impact of a subset of standard pre-processing strategies on different tasks. Our main goal is to raise awareness of non-standard content -- which should not always be considered as noise'' -- and of the need for careful, task-dependent pre-processing. This is an alternative to blanket, all-encompassing solutions generally applied by researchers through standard'' pre-processing pipelines. The intention is for this categorisation to serve as a point of reference to support NLP researchers in devising strategies to clean, normalise or embrace non-standard content.
We propose a method to learn contextualized and generalized sentence representations using contrastive self-supervised learning. In the proposed method, a model is given a text consisting of multiple sentences. One sentence is randomly selected as a
target sentence. The model is trained to maximize the similarity between the representation of the target sentence with its context and that of the masked target sentence with the same context. Simultaneously, the model minimizes the similarity between the latter representation and the representation of a random sentence with the same context. We apply our method to discourse relation analysis in English and Japanese and show that it outperforms strong baseline methods based on BERT, XLNet, and RoBERTa.
Abstractive conversation summarization has received much attention recently. However, these generated summaries often suffer from insufficient, redundant, or incorrect content, largely due to the unstructured and complex characteristics of human-huma
n interactions. To this end, we propose to explicitly model the rich structures in conversations for more precise and accurate conversation summarization, by first incorporating discourse relations between utterances and action triples (who-doing-what'') in utterances through structured graphs to better encode conversations, and then designing a multi-granularity decoder to generate summaries by combining all levels of information. Experiments show that our proposed models outperform state-of-the-art methods and generalize well in other domains in terms of both automatic evaluations and human judgments. We have publicly released our code at https://github.com/GT-SALT/Structure-Aware-BART.
The aim of this investigation is to explore the main rhetorical
features of an Arabic newspaper discourse. To this end, extracts form
two popular Jordanian newspapers were analyzed. The results of this
study indicate that one of the features of th
is type of discourse is
redundancy, i.e. repetition of the same lexical item. Another feature is
the explicit use of evaluative statements to support the writer’s point of
view. Moreover, the results of this study revealed that Arabic newspaper
discourse clearly marks clause relations especially subordinating clauses,
and that discourse markers are mainly used to mark the relationships of
contrast between or among propositions in this type of discourse.
Sensitivity of deep-neural models to input noise is known to be a challenging problem. In NLP, model performance often deteriorates with naturally occurring noise, such as spelling errors. To mitigate this issue, models may leverage artificially nois
ed data. However, the amount and type of generated noise has so far been determined arbitrarily. We therefore propose to model the errors statistically from grammatical-error-correction corpora. We present a thorough evaluation of several state-of-the-art NLP systems' robustness in multiple languages, with tasks including morpho-syntactic analysis, named entity recognition, neural machine translation, a subset of the GLUE benchmark and reading comprehension. We also compare two approaches to address the performance drop: a) training the NLP models with noised data generated by our framework; and b) reducing the input noise with external system for natural language correction. The code is released at https://github.com/ufal/kazitext.
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