Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userHow Will I Argue? A Dataset for Evaluating Recommender Systems for Argumentations
كيف سأقول؟مجموعة بيانات لتقييم أنظمة التوصية للجدول
220
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تعتبر عمليات تبادل الوسائط جزءا مهما في الاتصال، لكننا غالبا ما غمرت كثيرا مع الكثير من الحجج لمراكز مختلفة أو يتم التقاطها في فقاعات المرشح.الأدوات التي يمكن أن تقدم حجج قوية ذات صلة بالنفس يمكن أن تساعد في تقليل هذه المشاكل.لتكون قادرا على تقييم الخوارزميات التي يمكن أن تتنبأ بمدى مقنع الحجة، قمنا بجمع مجموعة بيانات بها أكثر من 900 حجج ومواقف شخصية تضم 600 فرد، والتي نقدمها في هذه الورقة.بناء على هذه البيانات، نقترح ثلاثة مهام توصية، التي نقدم النتائج التي نقدمها خطين أساسيين من مصنف أغلبية بسيطة وخوارزمية جارتين أكثر تعقيدا.تشير نتائجنا إلى أنه لا يزال من الممكن تطوير خوارزميات أفضل، وندعنا المجتمع لتحسين نتائجنا.
Exchanging arguments is an important part in communication, but we are often flooded with lots of arguments for different positions or are captured in filter bubbles. Tools which can present strong arguments relevant to oneself could help to reduce those problems. To be able to evaluate algorithms which can predict how convincing an argument is, we have collected a dataset with more than 900 arguments and personal attitudes of 600 individuals, which we present in this paper. Based on this data, we suggest three recommender tasks, for which we provide two baseline results from a simple majority classifier and a more complex nearest-neighbor algorithm. Our results suggest that better algorithms can still be developed, and we invite the community to improve on our results.
References used
https://aclanthology.org/
rate research
Read More
Counterfactual statements describe events that did not or cannot take place. We consider the problem of counterfactual detection (CFD) in product reviews. For this purpose, we annotate a multilingual CFD dataset from Amazon product reviews covering c
ounterfactual statements written in English, German, and Japanese languages. The dataset is unique as it contains counterfactuals in multiple languages, covers a new application area of e-commerce reviews, and provides high quality professional annotations. We train CFD models using different text representation methods and classifiers. We find that these models are robust against the selectional biases introduced due to cue phrase-based sentence selection. Moreover, our CFD dataset is compatible with prior datasets and can be merged to learn accurate CFD models. Applying machine translation on English counterfactual examples to create multilingual data performs poorly, demonstrating the language-specificity of this problem, which has been ignored so far.
Precisely defining the terminology is the first step in scientific communication. Developing neural text generation models for definition generation can circumvent the labor-intensity curation, further accelerating scientific discovery. Unfortunately
, the lack of large-scale terminology definition dataset hinders the process toward definition generation. In this paper, we present a large-scale terminology definition dataset Graphine covering 2,010,648 terminology definition pairs, spanning 227 biomedical subdisciplines. Terminologies in each subdiscipline further form a directed acyclic graph, opening up new avenues for developing graph-aware text generation models. We then proposed a novel graph-aware definition generation model Graphex that integrates transformer with graph neural network. Our model outperforms existing text generation models by exploiting the graph structure of terminologies. We further demonstrated how Graphine can be used to evaluate pretrained language models, compare graph representation learning methods and predict sentence granularity. We envision Graphine to be a unique resource for definition generation and many other NLP tasks in biomedicine.
In translating text where sentiment is the main message, human translators give particular attention to sentiment-carrying words. The reason is that an incorrect translation of such words would miss the fundamental aspect of the source text, i.e. the
author's sentiment. In the online world, MT systems are extensively used to translate User-Generated Content (UGC) such as reviews, tweets, and social media posts, where the main message is often the author's positive or negative attitude towards the topic of the text. It is important in such scenarios to accurately measure how far an MT system can be a reliable real-life utility in transferring the correct affect message. This paper tackles an under-recognized problem in the field of machine translation evaluation which is judging to what extent automatic metrics concur with the gold standard of human evaluation for a correct translation of sentiment. We evaluate the efficacy of conventional quality metrics in spotting a mistranslation of sentiment, especially when it is the sole error in the MT output. We propose a numerical sentiment-closeness'' measure appropriate for assessing the accuracy of a translated affect message in UGC text by an MT system. We will show that incorporating this sentiment-aware measure can significantly enhance the correlation of some available quality metrics with the human judgement of an accurate translation of sentiment.
We present GerDaLIR, a German Dataset for Legal Information Retrieval based on case documents from the open legal information platform Open Legal Data. The dataset consists of 123K queries, each labelled with at least one relevant document in a colle
ction of 131K case documents. We conduct several baseline experiments including BM25 and a state-of-the-art neural re-ranker. With our dataset, we aim to provide a standardized benchmark for German LIR and promote open research in this area. Beyond that, our dataset comprises sufficient training data to be used as a downstream task for German or multilingual language models.
This paper describes the annotation process of an offensive language data set for Romanian on social media. To facilitate comparable multi-lingual research on offensive language, the annotation guidelines follow some of the recent annotation efforts
for other languages. The final corpus contains 5000 micro-blogging posts annotated by a large number of volunteer annotators. The inter-annotator agreement and the initial automatic discrimination results we present are in line with earlier annotation efforts.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
