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Register a new userSharks are not the threat humans are: Argument Component Segmentation in School Student Essays
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Added by
Tariq Alhindi
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Argument mining is often addressed by a pipeline method where segmentation of text into argumentative units is conducted first and proceeded by an argument component identification task. In this research, we apply a token-level classification to identify claim and premise tokens from a new corpus of argumentative essays written by middle school students. To this end, we compare a variety of state-of-the-art models such as discrete features and deep learning architectures (e.g., BiLSTM networks and BERT-based architectures) to identify the argument components. We demonstrate that a BERT-based multi-task learning architecture (i.e., token and sentence level classification) adaptively pretrained on a relevant unlabeled dataset obtains the best results
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Recent advancements in neural language modelling make it possible to rapidly generate vast amounts of human-sounding text. The capabilities of humans and automatic discriminators to detect machine-generated text have been a large source of research interest, but humans and machines rely on different cues to make their decisions. Here, we perform careful benchmarking and analysis of three popular sampling-based decoding strategies---top-$k$, nucleus sampling, and untruncated random sampling---and show that improvements in decoding methods have primarily optimized for fooling humans. This comes at the expense of introducing statistical abnormalities that make detection easy for automatic systems. We also show that though both human and automatic detector performance improve with longer excerpt length, even multi-sentence excerpts can fool expert human raters over 30% of the time. Our findings reveal the importance of using both human and automatic detectors to assess the humanness of text generation systems.
Anthropic reasoning is a form of statistical reasoning based upon finding oneself a member of a particular reference class of conscious beings. By considering empirical distribution functions defined over animal life on Earth, we can deduce that the vast bulk of animal life is unlikely to be conscious.
Nowadays, open-domain dialogue models can generate acceptable responses according to the historical context based on the large-scale pre-trained language models. However, they generally concatenate the dialogue history directly as the model input to predict the response, which we named as the flat pattern and ignores the dynamic information flow across dialogue utterances. In this work, we propose the DialoFlow model, in which we introduce a dynamic flow mechanism to model the context flow, and design three training objectives to capture the information dynamics across dialogue utterances by addressing the semantic influence brought about by each utterance in large-scale pre-training. Experiments on the multi-reference Reddit Dataset and DailyDialog Dataset demonstrate that our DialoFlow significantly outperforms the DialoGPT on the dialogue generation task. Besides, we propose the Flow score, an effective automatic metric for evaluating interactive human-bot conversation quality based on the pre-trained DialoFlow, which presents high chatbot-level correlation ($r=0.9$) with human ratings among 11 chatbots. Code and pre-trained models will be public. footnote{url{https://github.com/ictnlp/DialoFlow}}
We study citation dynamics of the Physics, Economics, and Mathematics papers published in 1984 and focus on the fraction of uncited papers in these three collections. Our model of citation dynamics, which considers citation process as an inhomogeneous Poisson process, captures this uncitedness ratio fairly well. It should be noted that all parameters and variables in our model are related to citations and their dynamics, while uncited papers appear as a byproduct of the citation process and this is the Poisson statistics which makes the cited and uncited papers inseparable. This indicates that the most part of uncited papers constitute the inherent part of the scientific enterprise, namely, uncited papers are not unread.
A small fraction of intermediate-mass main sequence (A and B type) stars have strong, organised magnetic fields. The large majority of such stars, however, show no evidence for magnetic fields, even when observed with very high precision. In this paper we describe a simple model, motivated by qualitatively new observational results, that provides a natural physical explanation for the small fraction of observed magnetic stars.
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