Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userHelp! Need Advice on Identifying Advice
72
0
0.0
(
0
)
Added by
Venkata Subrahmanyan Govindarajan
Publication date
2020
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Humans use language to accomplish a wide variety of tasks - asking for and giving advice being one of them. In online advice forums, advice is mixed in with non-advice, like emotional support, and is sometimes stated explicitly, sometimes implicitly. Understanding the language of advice would equip systems with a better grasp of language pragmatics; practically, the ability to identify advice would drastically increase the efficiency of advice-seeking online, as well as advice-giving in natural language generation systems. We present a dataset in English from two Reddit advice forums - r/AskParents and r/needadvice - annotated for whether sentences in posts contain advice or not. Our analysis reveals rich linguistic phenomena in advice discourse. We present preliminary models showing that while pre-trained language models are able to capture advice better than rule-based systems, advice identification is challenging, and we identify directions for future research. Comments: To be presented at EMNLP 2020.
rate research
Read More
Due to their unique persuasive power, language-capable robots must be able to both act in line with human moral norms and clearly and appropriately communicate those norms. These requirements are complicated by the possibility that humans may ascribe blame differently to humans and robots. In this work, we explore how robots should communicate in moral advising scenarios, in which the norms they are expected to follow (in a moral dilemma scenario) may be different from those their advisees are expected to follow. Our results suggest that, in fact, both humans and robots are judged more positively when they provide the advice that favors the common good over an individuals life. These results raise critical new questions regarding peoples moral responses to robots and the design of autonomous moral agents.
Is it possible to use natural language to intervene in a models behavior and alter its prediction in a desired way? We investigate the effectiveness of natural language interventions for reading-comprehension systems, studying this in the context of social stereotypes. Specifically, we propose a new language understanding task, Linguistic Ethical Interventions (LEI), where the goal is to amend a question-answering (QA) models unethical behavior by communicating context-specific principles of ethics and equity to it. To this end, we build upon recent methods for quantifying a systems social stereotypes, augmenting them with different kinds of ethical interventions and the desired model behavior under such interventions. Our zero-shot evaluation finds that even todays powerful neural language models are extremely poor ethical-advice takers, that is, they respond surprisingly little to ethical interventions even though these interventions are stated as simple sentences. Few-shot learning improves model behavior but remains far from the desired outcome, especially when evaluated for various types of generalization. Our new task thus poses a novel language understanding challenge for the community.
The bin covering problem asks for covering a maximum number of bins with an online sequence of $n$ items of different sizes in the range $(0,1]$; a bin is said to be covered if it receives items of total size at least 1. We study this problem in the advice setting and provide tight bounds for the size of advice required to achieve optimal solutions. Moreover, we show that any algorithm with advice of size $o(log log n)$ has a competitive ratio of at most 0.5. In other words, advice of size $o(log log n)$ is useless for improving the competitive ratio of 0.5, attainable by an online algorithm without advice. This result highlights a difference between the bin covering and the bin packing problems in the advice model: for the bin packing problem, there are several algorithms with advice of constant size that outperform online algorithms without advice. Furthermore, we show that advice of size $O(log log n)$ is sufficient to achieve a competitive ratio that is arbitrarily close to $0.53bar{3}$ and hence strictly better than the best ratio $0.5$ attainable by purely online algorithms. The technicalities involved in introducing and analyzing this algorithm are quite different from the existing results for the bin packing problem and confirm the different nature of these two problems. Finally, we show that a linear number of bits of advice is necessary to achieve any competitive ratio better than 15/16 for the online bin covering problem.
The priority model of greedy-like algorithms was introduced by Borodin, Nielsen, and Rackoff in 2002. We augment this model by allowing priority algorithms to have access to advice, i.e., side information precomputed by an all-powerful oracle. Obtaining lower bounds in the priority model without advice can be challenging and may involve intricate adversary arguments. Since the priority model with advice is even more powerful, obtaining lower bounds presents additional difficulties. We sidestep these difficulties by developing a general framework of reductions which makes lower bound proofs relatively straightforward and routine. We start by introducing the Pair Matching problem, for which we are able to prove strong lower bounds in the priority model with advice. We develop a template for constructing a reduction from Pair Matching to other problems in the priority model with advice -- this part is technically challenging since the reduction needs to define a valid priority function for Pair Matching while respecting the priority function for the other problem. Finally, we apply the template to obtain lower bounds for a number of standard discrete optimization problems.
The priority model was introduced by Borodin, Rackoff, and Nielsen (2003) to capture greedy-like algorithms. Motivated by the success of advice complexity in the area of online algorithms, Borodin et al. (2020) extended the fixed priority model to include an advice tape oracle. They also developed a reduction-based framework for proving lower bounds on the amount of advice required to achieve certain approximation ratios in this rather powerful model. In order to capture most of the algorithms that are considered greedy-like, the even stronger model of adaptive priority algorithms is needed. We extend the adaptive priority model to include an advice tape oracle. We show how to modify the reduction-based framework from the fixed priority case, making it applicable to the more powerful adaptive priority algorithms. The framework provides a template, where one can obtain a lower bound relatively easily by exhibiting gadget patterns fulfilling given criteria. In the process, we simplify the proof that the framework works, and we strengthen all the earlier lower bounds by a factor two. As a motivating example, we present a purely combinatorial adaptive priority algorithm with advice for Minimum Vertex Cover on triangle-free graphs of maximum degree three. Our algorithm achieves optimality and uses at most 7n/22 bits of advice. Known results imply that no adaptive priority algorithm without advice can achieve optimality without advice, and we prove that 7n/22 is fewer bits than an online algorithm with advice needs to reach optimality. Furthermore, we show connections between exact algorithms and priority algorithms with advice. Priority algorithms with advice that achieve optimality can be used to define corresponding exact algorithms, priority exact algorithms. The lower bound templates for advice-based adaptive algorithms imply lower bounds on exact algorithms designed in this way.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
