Tag Archives: chatbots

Disinhibition and Human Computer Interaction

For some reason, when we are learning a foreign language, we feel intimidated to speak it. We fear we will be laughed at, won’t say the right thing and won’t be understood or simply lack the confidence to put a voice to the words floating around in our brains forming utterances.

It is clear inhibition to speak is a common problem among language learners for whatever reason. So, I am investigating strategies to disinhibit learners, and to provide them with oral interaction confidence, by engaging with a computer to practice speaking, so they have the confidence to interact with humans.

Human computer interaction (HCI) to practise English conversation offers several advantages compared to practising with a human. The main motivations being:

  •  low inhibition because learners know are they are interacting with a machine that will not judge their performance unless asked to do so.
  • a low-anxiety environment which promotes confidence because of the absence of a human waiting form the next turn.
  • Interaction for as long as the learner wants to practice.
  • Computers do not lose their patience, or tire of conversing or repeating the same conversation pattern.

I therefore strongly believe that HCI is a promising solution for learner disinhibitition.Updates on experiments carried out with chatbots to fulfil this research to follow…

Man or machine?

Man or machine? That is the question! There is an endless flow of information being pushed onto our screens about the danger of robots and machines taking over the world. Martin Ford’s Rise of the robots (2015) presents a blatantly bleak view of automation and the ‘threat of jobless future’ due to the advances of technology.

When it comes to automated customer service agents, I am sure we all have long winded stories of negative experiences. On the flip-side however, I have also had my share of less than favourable customer service experiences with humans. While there is evidence of the frustrations of not being able to interact with a human to resolve customer service issues, there is considerably more evidence which supports the view that the human was unable to resolve the query, and a chatbot could have more than adequately dealt with the matter in a considerably shorter time frame (Xu et al, 2017). Chatbots are also consistently patient and polite; remain unruffled by rude customers, high traffic, or repeated requests for the same information, and never tire (McNeal & Newyear, 2013).

I think there is a time and a place for everything. But given the inflated lack of patience and the abundance of immediacy that humans expect from the service sector nowadays, I think chatbots are a good option for quick enquiries and to resolve systematic ‘problems’.


From RALL to chatbots

I began the year with a strong desire to continue my research into RALL, and while that is still the case, my research has lead me to investigate the benefits and  pedagogical potential of using chatbot teachers to assist in language learning.

The research examines the use of a speech-to-speech interface as the language-learning tool, designed with the specific intention of promoting oral interaction in English. The computer (chatbot) will assume the role of conversational partner, allowing the learner to practice conversing in English. A retrieval-based model will be used to select appropriate output from predefined responses. This model will then be mapped onto a gamification framework to ensure an interesting and engaging interactional experience.

Speech is one of the most powerful forms of communication between humans; hence, it is my intention to add to current research in the human-computer interaction research field to improve speech interaction between learners and the conversational agent (the chatbot) in order to simulate human-human speech interaction.

So just how should you speak to a chatbot?

So just how should you speak to a chatbot? If you cast your mind back to Tay the chatbot built by Microsoft. She was shut down on the grounds of inappropriateness because she was posting offensive and unsuitable content on her Twitter account. Hardly surprising really considering she was built using corpus from Twitter posts and dialogues, a perfect example of the hunter becoming the hunted.  

The apparent ubiquity of chatbots in the customer service sector is proving to be somewhat beneficial to the companies using them, but less convenient for users. The majority of conversation agents are built using a retrieval-based model, which reply based on a repository of predefined responses from which the most appropriate is selected, based on the input text and context. The output could be limited to as little as three utterances per response. Let’s look at an opening turn to see how this works:

‘Hello, what can I do for you today?’

> No response, delayed response from user, or the chatbot is unable to interpret the user input.

‘I missed that, say that again?’

> No response, delayed response from user, or the chatbot is unable to interpret the user input.

‘Sorry, can you say that again?’

> No response, delayed response from user, or the chatbot is unable to interpret the user input.

‘Sorry, I can’t help.’.

This leads me to believe that we as users need to learn how to speak to an automated conversation agent before determining what we want from it. If we don’t respond, or respond using undecipherable discourse then we are expecting a machine to manage a task that humans would also face problems with interpreting. While considerable research and development is being carried out in the field of intelligent conversational agents, we are still a long way from them becoming an integral part of mainstream customer service interfaces that are able to interpret our utterances and commands to the best of our expectations.

Turn taking and chatbots

Turn taking is a natural part of conversation that we subconsciously engage in order for the discourse to flow. Here is an example:

A: “Good morning”

B: “Morning. How are you? Good weekend?”

A: “Yes thanks, and you? How was Brighton?”

For the Cambridge main suite speaking exams, candidates are assessed on their turn-taking ability under the criteria of ‘Interactive Communication’. In other words, this means the candidates’ ability to:

  • Interact with the other candidate easily and effectively.
  • Listening to the other candidate and answering in a way that makes sense.
  • The ability to start a discussion and keep it going with their partner/s.
  • The ability to think of new ideas to add to the discussion.

Along with the onslaught of technological advances came advance in automated responses from portable digital devices. These conversational agents or dialogue systems are capable of single interactions or up to 6 task-oriented turns. An example of these dialogue agents would be Siri, and an example of a talk-oriented interaction would be: “Siri call Dad”.

Chatbots are not a ‘new’ invention per say. Eliza, created between 1964-1966 at the MIT was a natural language processing computer programme that demonstrated the same characteristics of chatbots day, but on a less sophisticated scale, and with less complex interaction. The aim of chatbot builders is to create natural language processing programmes that replicate human-human interaction by enabling more turns and therefore extended conversations.

The interesting challenge then becomes, how to use each turn as a springboard for the next, and ensure that each one prompts a response that has been pre-programmed, in order not to receive a generic message like: “I’m sorry, but I’m not sure what you mean by that”, when the user is expressing a specific request or a expressing a turn that is not recognised. More about chatbots soon!

Just what do we expect from Chatbots?

Chatbots are the future of conversation intelligence, and can be used to stimulate human conversations. But just what do we expect from chatbots? On the one hand are those that firmly believe intelligent systems will dissipate the element of human interaction in years to come. On the other hand others revel in the delights of giving Siri instructions to challenge her intelligence and gauge the level of response.

Personally I feel that benefits for intelligent systems (chatbots) outweigh the disadvantages, but I am convinced that the advantages will depend on our behaviour and receptiveness to accept their merits. AI cynics were delighted when Microsoft’s Tay was morphed to demonstrate bad behaviour. At last there was proof to substantiate the argument in favour of the severe dangers of AI.

Users of Alexa were slightly disturbed by her random outbursts of laughter, to the extent that her code was re-written to disable a reaction when requested to do so, and to avoid reactions to false positives that try to trick her. This all leads to the question of the levels of humanness we expect from intelligent systems and chatbots, or more to the point the level of humanness we, as ‘humans’, are comfortable with accepting from ‘machines’.

AI: a new currency or the next industrial revolution?

A question that has been on my lips recently is whether AI is set to be the next industrial revolution, or a new currency of the future.

AI Past

The industrial revolution as its name denotes, revolutionised modern industry and manufacturing as we know it today. When the internet emerged in the late 1980s it seemed unimaginable that less than 30 years later, wireless connections and digital devices would have such a pervasive presence in society. New inventions come and go, and technological innovations are created whether they are successful or not, but in most cases they are shaped by the demands of people.

The origins of AI date back to Turing’s computational machine more commonly known as the Turning machine, built in 1935, however the term was coined later in 1955 by McCarthy who defined it as “the science and engineering of making intelligent machines, especially intelligent computer programmes” (ibid 2001:02), in other words trying to understand human intelligence by using computers.

AI Present

During the last 80 years, advances in AI technology have reached astounding levels. It has clearly had a prolific impact on society, to the extent that it has been transformed into a tool in all aspects of life; from banking and email pop ups, to ‘personalised’ selected products, and Siri and Alexa the intelligent personal assistants, and chatbots.

AI Future

Both academic and business investigation and reporting in the field of AI, consider it to be one of the biggest influencers for the future of the market and society. Predicted revenues from AI are unprecedented, resulting in extensive funding and investment from private companies and governments, which highlights the significance of AI in society. China has recently announced they are building a $2.1 billion industrial park for AI research. The past year has witnessed an increasing amount of nations realising the importance of AI in shaping the economics of the future, some even consider it a currency. Bitcoins stand aside, AI is the new currency..