Summary:
This paper proposes a new system. The system can combine crowdsourcing workers with machine learning to get better chat robots. The motivation for the authors to propose this idea is: fully automatic chat robots usually do not respond as well as crowd-driven conversation assistants, which is also obvious in our lives. But crowd-driven conversation assistants have higher cost and longer response time. So the author built the Evorus system, which is a crowd support conversation assistant. There are three main ways to achieve more efficient:
- This new system can integrate other chat robots;
- It can reuse previous answers;
- It can learn to automatically approve responders;
In short, when users chat with the system, users can evaluate the response, when the response is not ideal, the system can quote the answers from the crowdsourcing workers, and at the same time, it can learn the Q & A, and take the next time to answer the similar questions. In the direction that has been practiced, you can quickly answer questions. This speeds up Q & A, but also improves accuracy.
Reflection:
I believe that in daily life, people will definitely have access to a large number of automated question answering systems. For example, when going to the official website of UPS, there will be a chat robot popping up to ask your purpose, but usually there are only a few directions, However, when people’s requirements tend to be complicated, the chat tends to be complicated, and the automatic answering robot cannot handle it, which will make users go to phone consultation or other homepage. I think this response mainly comes from pre-booked questions and answers, so I think the system proposed by the author has a very important use value.
At the same time, I think that the biggest advantage of this system’s answer through the self-learning crowdsourcing system is that it can be updated continuously in time. Frequent updates usually consume a lot of manpower and resources, and timely updates are more important in communication tools. In network, the terminology and emerging vocabulary are updated very quickly. If it can be updated frequently by studying the accepted answers in each response which need crowdsourcing workers engage, it will have a very positive impact on system maintenance and users.
Finally, the system introduces the answering system into a wider field, not only in the way of updating, revising, and answering questions, but also more importantly, combining humans and machines, and opening a sealing systems to make it can be continuously updated. And more and more innovative projects will be added to it, which is what I think is more meaningful than the system itself.
Question:
- Do you think there are any other fields this system can add?
- How to evaluate crowdsourcing workers response, in other words how to make sure crowdsourcing workers’ response is better than machine.
- What is the difference between the system mentioned in this paper with other Q&A machine in modern society.
Hello, interesting reflections and discussion points! Regarding the second discussion point, “How to evaluate crowdsourcing workers response, in other words how to make sure crowdsourcing workers’ response is better than a machine?”, I feel that the system relies on the fact that humans have a more nuanced understanding of the context and questions asked and would be able to interpret them in ways a machine may not be able to. Having said that, in this specific case, crowd workers are being used and ensuring quality in this setting is altogether a different and difficult challenge. Appropriate motivation and incentives are needed to ensure the proper engagement of the workers in this context.