instruction:
In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
question:
Accurate prediction of suitable discourse connectives (however, furthermore, etc.) is a key component of any system aimed at building coherent and fluent discourses from shorter sentences and passages. As an example, a dialog system might assemble a long and informative answer by sampling passages extracted from different documents retrieved from the web. We formulate the task of discourse connective prediction and release a dataset of 2.9M sentence pairs separated by discourse connectives for this task. Then, we evaluate the hardness of the task for human raters, apply a recently proposed decomposable attention (DA) model to this task and observe that the automatic predictor has a higher F1 than human raters (32 vs. 30). Nevertheless, under specific conditions the raters still outperform the DA model, suggesting that there is headroom for future improvements. Finally, we further demonstrate the usefulness of the connectives dataset by showing that it improves implicit discourse relation prediction when used for model pre-training.
answer:
Automatic Prediction of Discourse Connectives


question:
Analysing sentiment of tweets is important as it helps to determine the users’ opinion. Knowing people’s opinion is crucial for several purposes starting from gathering knowledge about customer base, e-governance, campaignings and many more. In this report, we aim to develop a system to detect the sentiment from tweets. We employ several linguistic features along with some other external sources of information to detect the sentiment of a tweet. We show that augmenting the 140 character-long tweet with information harvested from external urls shared in the tweet as well as Social Media features enhances the sentiment prediction accuracy significantly.
answer:
Sentiment Analysis for Twitter : Going Beyond Tweet Text


question:
It is natural and efficient to use Natural Language (NL) for transferring knowledge from a human to a robot. Recently, research on using NL to support human-robot cooperation (HRC) has received increasing attention in several domains such as robotic daily assistance, robotic health caregiving, intelligent manufacturing, autonomous navigation and robot social accompany. However, a high-level review that can reveal the realization process and the latest methodologies of using NL to facilitate HRC is missing. In this review, a comprehensive summary about the methodology development of natural-language-facilitated human-robot cooperation (NLC) has been made. We first analyzed driving forces for NLC developments. Then, with a temporal realization order, we reviewed three main steps of NLC: human NL understanding, knowledge representation, and knowledge-world mapping. Last, based on our paper review and perspectives, potential research trends in NLC were discussed.
answer:

Methodologies realizing natural-language-facilitated human-robot cooperation: A review