Chatbot’s potential is nothing new, and here at Bitext we have been talking about them for a while. We emphasize the importance of Natural Language Processing to overcome the current limitations users and developers of bots are facing when trying to create human-like chatbots.
We have done some testing of bots and bot developing platforms like wit.ai, api.ai, and LUIS. And we detected some issues that seem not to be completely solved yet. The issues found are fundamental for human language and our linguistic technology is a perfect match to solve them.
We decided to put our resources to work and today we are proud to introduce you to our Chatbot interactive infography so you can start having an idea of what our platform can do.
The demo proposes that you follow different sentences within a conversation. The sentences contain different issues we have detected while testing bots and platforms:
- Negation: we realized that many bots don’t understand negation in a phrase because they have been built based on a keyword approach. That makes it difficult for users to ask for something as simple as “I want a barbeque pizza with no pork”. Let's see some examples:
- “I want a barbeque pizza with no pork” (only negates pork).
- “We don’t want any drinks” (negates the whole event).
- “I’m not sure… I’ll take a beer (It doesn’t negate the main event)”
- Coordination: it is one of the most used elements in how humans talk, and after our research we found out that most relevant platforms do not support a request where elements are joined by a coordinator. Our linguistic knowledge makes us capable to solve this issue.
- “[[I want a Hawaiian pizza] and [my wife will have a Margherita]]” (two main events).
- “I’ll have a Hawaiian [with [extra cheese] and [onion]] (two changes in ingredients)”.
- “I’ll take [[a Hawaiian [with [extra cheese] and [onion]]] and [a Margherita]]” (two pizzas, the first one with two ingredients)
- Connection between different phrases: Most of the chatbots have been designed following a tree model, so it’s not possible for a user to change his request and that forces him to start over. As solution we propose the usage of connectors as in the following examples:
- “I want a Margherita with onion… Moreover, add extra cheese” (adds info to the first sentence: adds an ingredient).
- “I want a Hawaiian with extra pineapple. However, I prefer it with no ham” (also adds info to the previous one).
During the demo, you will see not only the conversation flow but also the text structure and linguistic knowledge that we can extract from raw text. Text structure is key to understand requests and answer properly. That linguistic knowledge is shown in the demo as a parse tree and a JSON with the semantic information that can be derived from it. This feature is key because it’s our parsing technology what allows us to extract relevant information that can be used by Machine Learning to extract the intent and to give the appropriate answer.
If you want to start trying our interactive chatbot infographic demo click here: