1,706 research outputs found
Retrospective Reader for Machine Reading Comprehension
Machine reading comprehension (MRC) is an AI challenge that requires machine
to determine the correct answers to questions based on a given passage. MRC
systems must not only answer question when necessary but also distinguish when
no answer is available according to the given passage and then tactfully
abstain from answering. When unanswerable questions are involved in the MRC
task, an essential verification module called verifier is especially required
in addition to the encoder, though the latest practice on MRC modeling still
most benefits from adopting well pre-trained language models as the encoder
block by only focusing on the "reading". This paper devotes itself to exploring
better verifier design for the MRC task with unanswerable questions. Inspired
by how humans solve reading comprehension questions, we proposed a
retrospective reader (Retro-Reader) that integrates two stages of reading and
verification strategies: 1) sketchy reading that briefly investigates the
overall interactions of passage and question, and yield an initial judgment; 2)
intensive reading that verifies the answer and gives the final prediction. The
proposed reader is evaluated on two benchmark MRC challenge datasets SQuAD2.0
and NewsQA, achieving new state-of-the-art results. Significance tests show
that our model is significantly better than the strong ELECTRA and ALBERT
baselines. A series of analysis is also conducted to interpret the
effectiveness of the proposed reader.Comment: Accepted by AAAI 202
Emoticon-based Ambivalent Expression: A Hidden Indicator for Unusual Behaviors in Weibo
Recent decades have witnessed online social media being a big-data window for
quantificationally testifying conventional social theories and exploring much
detailed human behavioral patterns. In this paper, by tracing the emoticon use
in Weibo, a group of hidden "ambivalent users" are disclosed for frequently
posting ambivalent tweets containing both positive and negative emotions.
Further investigation reveals that this ambivalent expression could be a novel
indicator of many unusual social behaviors. For instance, ambivalent users with
the female as the majority like to make a sound in midnights or at weekends.
They mention their close friends frequently in ambivalent tweets, which attract
more replies and thus serve as a more private communication way. Ambivalent
users also respond differently to public affairs from others and demonstrate
more interests in entertainment and sports events. Moreover, the sentiment
shift of words adopted in ambivalent tweets is more evident than usual and
exhibits a clear "negative to positive" pattern. The above observations, though
being promiscuous seemingly, actually point to the self regulation of negative
mood in Weibo, which could find its base from the emotion management theories
in sociology but makes an interesting extension to the online environment.
Finally, as an interesting corollary, ambivalent users are found connected with
compulsive buyers and turn out to be perfect targets for online marketing.Comment: Data sets can be downloaded freely from www.datatang.com/data/47207
or http://pan.baidu.com/s/1mg67cbm. Any issues feel free to contact
[email protected]
Test-Case-Driven Programming Understanding in Large Language Models for Better Code Generation
Code generation is to automatically generate source code conforming to a
given programming specification, which has received extensive attention
especially with the development of large language models (LLMs). Due to the
inherent difficulty of code generation, the code generated by LLMs may be also
not aligned with the specification. To improve the perfor mance of LLMs in code
generation, some Chain of Thought (CoT) techniques have been proposed to guide
LLMs for programming understanding before code generation. However, they are
still hard to figure out complicated programming logic according to the
(concise) specification, leadingto unsatisfactory code generation performance.
In this work, we propose the first test-case-driven CoT technique, called TCoT,
to further enhance the ability of LLMs in code generation. It understands the
programming specification from the novel perspective of test cases, which is
aligned with human practice by using examples to understand complicated
problems. Due to the existence of the expected output specified in a test case,
TCoT can instantly check the correctness of the programming understanding and
then refine it to be as correct as possible before code generation. In this
way, it is more likely to generate correct code. Our evaluation on 6 datasets
and 14 baselines demonstrates the effectiveness of TCoT. For example, TCoT
improves ChatGPT by 13.93%~69.44% in terms of Pass@1 (measuring the ratio of
programming problems for which the generated code passes all test cases), and
outperforms the existing CoT technique with the improvement of 12.14%~53.72% in
terms of Pass@1
Exploration on the Internationalization of Basic Education From the Perspective of Globalization
Globalization is a factor which is objective and an orientation in the process of world development, in which the internationalization of education has become the trend of the times.Besides, the economic development, which is fast and the highly developed information and deeply knowledge in the current era have made the internationalization of basic education an important direction for future development and an important reference for realizing educational cooperation, exchange and sharing. In the view of globalization and the situation of continuous collision, interchange and integration of education stick to the tide of internationalization of education, contraposing the internationalization of basic education putting forward specific strategies to standardize, guide the internationalization of basic education over the long haul to provide referable suggestions
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