CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning

To accelerate software development, much research has been performed to help people understand and reuse the huge amount of available code resources. Two important tasks have been widely studied: code retrieval, which aims to retrieve code snippets relevant to a given natural language query from a code base, and code annotation, where the goal is to annotate a code snippet with a natural language description. Despite their advancement in recent years, the two tasks are mostly explored separately. In this work, we investigate a novel perspective of Code annotation for Code retrieval (hence called `CoaCor'), where a code annotation model is trained to generate a natural language annotation that can represent the semantic meaning of a given code snippet and can be leveraged by a code retrieval model to better distinguish relevant code snippets from others. To this end, we propose an effective framework based on reinforcement learning, which explicitly encourages the code annotation model to generate annotations that can be used for the retrieval task. Through extensive experiments, we show that code annotations generated by our framework are much more detailed and more useful for code retrieval, and they can further improve the performance of existing code retrieval models significantly.Comment: 10 pages, 2 figures. Accepted by The Web Conference (WWW) 201

Leveraging 2-hop Distant Supervision from Table Entity Pairs for Relation Extraction

Distant supervision (DS) has been widely used to automatically construct (noisy) labeled data for relation extraction (RE). Given two entities, distant supervision exploits sentences that directly mention them for predicting their semantic relation. We refer to this strategy as 1-hop DS, which unfortunately may not work well for long-tail entities with few supporting sentences. In this paper, we introduce a new strategy named 2-hop DS to enhance distantly supervised RE, based on the observation that there exist a large number of relational tables on the Web which contain entity pairs that share common relations. We refer to such entity pairs as anchors for each other, and collect all sentences that mention the anchor entity pairs of a given target entity pair to help relation prediction. We develop a new neural RE method REDS2 in the multi-instance learning paradigm, which adopts a hierarchical model structure to fuse information respectively from 1-hop DS and 2-hop DS. Extensive experimental results on a benchmark dataset show that REDS2 can consistently outperform various baselines across different settings by a substantial margin

ドーパミン受容体Dop1R2はRaf/MAPK経路を介して嗅覚記憶を安定化する

Tohoku University谷本拓課

Understanding the innovation-driven sustainability of Chinese SMEs from central China: a missing piece of the jigsaw

This research addressed the question “How CSMEs achieve systematic IDS?” and aimed to develop a systematic and comprehensive theoretical understanding in relation to the CSME’s IDS definition, motivation, adoption and measurement. It had become significant that this was under-researched in prior studies. In particular, the prior studies in the innovation and sustainability domain have mainly focused on the antecedents of SMEs’ sustainable behaviours, like barriers and drivers, whereas the process of how innovation-driven behaviours can lead to sustainability for SMEs and the relevant measuring criteria were under-researched. In addition, research has been mainly focused on Western SMEs from Europe and North America (Moon & Shen, 2010), and the mainstream theory has largely focused on large enterprises (Halme & Korpela, 2014); therefore, SMEs’ IDS was less well-understood and documented, particularly in the context of emerging markets and economies, such as China. The interpretivism philosophy that the researcher held encouraged her to choose an inductive method and conduct qualitative research, followed by choosing a social constructionism epistemological perspective and then adapting the multi-case study method as the research strategy for this study. The semi-structured interview was used to collect qualitative data, whilst secondary data was collected to supplement the primary research. Overall, 54 people were interviewed, and multi-case studies were conducted that cross-analysed 12 SMEs. King and Brooks’ (2017) model was used to guide the process of template analysis. Based on empirical evidence and template analysis, a systematic and comprehensive understanding in relation CSMEs’ systematic IDS emerged. In detail, several key themes, including CS definition in China’s context, the relationship between innovation and CS, the motives, the actions in each adoption stage and their influencing factors, as well as the measurements, were explored in-depth and analysed. By applying multi-level analysis and interpreting data from different theoretical perspectives, the findings from this research filled the identified research gaps and expanded the knowledge of SMEs’ IDS. Furthermore, the findings of this study offered practical and systematic methods for practitioners such as SMEs in unfavourable regions to achieve CS towards innovation. And other stakeholders who want to engage in this process in the future actively, such as the Chinese Government or supply chain players, can have a better recognition of their role. In addition, for researchers who will select China as their targeted context, this research, especially the methodology chapter and appendixes, discussed and presented a practical research method to collect and analyse empirical evidence there

Can ChatGPT Defend its Belief in Truth? Evaluating LLM Reasoning via Debate

Large language models (LLMs) such as ChatGPT and GPT-4 have shown impressive performance in complex reasoning tasks. However, it is difficult to know whether the models are reasoning based on deep understandings of truth and logic, or leveraging their memorized patterns in a relatively superficial way. In this work, we explore testing LLMs' reasoning by engaging with them in a debate-like conversation, where given a question, the LLM and the user need to discuss to make the correct decision starting from opposing arguments. Upon mitigating the Clever Hans effect, our task requires the LLM to not only achieve the correct answer on its own, but also be able to hold and defend its belief instead of blindly believing or getting misled by the user's (invalid) arguments and critiques, thus testing in greater depth whether the LLM grasps the essence of the reasoning required to solve the problem. Across a range of complex reasoning benchmarks spanning math, commonsense, logic and BIG-Bench tasks, we find that despite their impressive performance as reported in existing work on generating correct step-by-step solutions in the beginning, LLMs like ChatGPT cannot maintain their beliefs in truth for a significant portion of examples when challenged by oftentimes absurdly invalid arguments. Our work points to danger zones of model alignment, and also suggests more careful treatments and interpretations of the recent findings that LLMs can improve their responses based on feedback.Comment: EMNLP-23 (findings

China and the Fifth Estate: Net Delusion or Democratic Potential?

Arguably, liberal democratic societies are seeing the emergence of a ‘Fifth Estate’ that is being enabled by the Internet. This new organizational form is comparable to, but potentially more powerful than, the Fourth Estate, which developed as a significant force in an earlier period with an independent press and other mass media. While the significance of the press and the Internet to democratic governance is questioned in all societies, there is particular skepticism of their relevance outside the most liberal democratic regimes, which have a relatively free press and more pluralistic political systems, such as in North America and West Europe. Nevertheless, there have been vivid examples of where networked individuals have appeared to assert greater communicative power in the politics of governance, the media and everyday life, even in non-liberal democratic regimes, such as Hong Kong, and in some cases, China. This potential points to the need for more systematic empirical research in a wider variety of economic and political settings worldwide, particularly in states in which the Internet might offer a potential for more democratic governance and greater accountability of government controlled media. This paper examines cases in which networked individuals in China used the Internet to hold governmental and press institutions more accountable. The cases provide support for the relevance of the Fifth Estate concept in China, and also illuminates the process – showing how the Internet can be used to empower networked individuals in more autocratic regimes