Natural Language Processing: Powers the Future of Voice Bot Conversations

Interacting with voice assistants using speech feels very futuristic. However, most conversations still feel quite rigid and limited compared to chatting with a real human. With new advances in artificial intelligence (AI) like natural language processing consulting, how close are we to having voice bots that can handle truly free-flowing dialogue?

Natural Language Processing: Teaching Chatbots to Understand Context

The voice assistants we use today such as Alexa, Siri and Google Assistant rely on fairly simple Natural Language Processing approaches to parse the commands and questions we ask them. Typically, the bot recognizes certain keywords and patterns in your input to match predefined intents that the developers configured. This works well enough for straightforward requests like setting a timer, checking the weather, or adding items to your shopping list. The assistant picks out the key details, maps it to the appropriate action, returns the information, and moves on.

However, conversations lose coherence very quickly without a broader contextual understanding of the actual topic being discussed. If you ask a follow-up question or make a related request, today’s assistants usually treat it as a brand-new, unrelated exchange. Without the context, the bots often fail to clarify ambiguous follow-up statements correctly or just repeat the same pre-programmed conversational script over again.

Natural Language Processing - Teaching Chatbots

This forces users to over-articulate every single demand with all details instead of progressing logically as the dialogue builds. Exciting advances in natural language processing incorporate more sophisticated neural networks to dynamically build richer contextual representations as a conversation unfolds. Essentially, chatbots can now train on massive volumes of realistic human-human conversational data to learn how language and topics naturally flow back and forth.

Their models predict unstated context based on previous exchanges to fill gaps in understanding accurately. Thus, instead of just rigidly analyzing utterances in total isolation, the assistant tracks the evolving contextual state across the dialogue history. As these natural language processing models continue maturing, voice assistants should be able to hold memory of key entities and facts that were mentioned earlier in the chat.

This prevents frustrating repetitions where the user has to provide the same information again because the bot failed to retain it. Over time, the natural language processing advancements will allow vocal interactions to reference concepts discussed much earlier with clarity.

Adding Common Sense through Pre-Training

In addition to vocabulary knowledge itself, our human conversations rely extensively on accumulated common sense and shared understanding of the world formed over decades of experiencing life. Unfortunately, today’s voice assistants are notoriously lacking in awareness of basic everyday logic, events, concepts, and social dynamics that we subconsciously take for granted when talking to another person.

For example, if you tell your friend that you’re taking your daughter’s dress shopping this weekend for a wedding, the human automatically deduces you have a daughter. Understands the approximate age range if going to weddings, knows that dresses are common formalwear for such occasions etc. But a voice assistant blindly only registers the literal keywords with no broader inferences or connections supplied by common sense.

Managing Complex, Multi-Turn Conversations

Human verbal exchanges rarely follow clean, linear frameworks for long – we regularly switch topics abruptly, jump between vaguely connected tangents, flip focus midway, and then circle back to earlier themes later in the conversation. Despite this, we can retain overall coherence and logical flow through natural language processing. This ability depends on intuitively understanding the other person’s viewpoint moment-by-moment based on unique, human-like conversational patterns.

A Detail Functioning of Natural Language Processing in Today’s Era

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Early voice helpers and chatbots struggled greatly trying to link disjointed statements or maintain the right situation context when users had complex interactions with lots of topic changes. Without the step-by-step, branched dialogue trees, the bots failed to remember important details from earlier chats or figure out how the shifting dialogue connected logically.

However, the next-gen conversation AI systems aim to solve this using a few methods. First, they add templates representing common discussion patterns like asking for clarification, switching subjects, restating concepts and refocusing. Additionally, the bots improve overall memory capabilities to recall details across time and chat turns more reliably.

Together, these allow voice assistants to ask smarter confirming follow-up questions while tying together previous scattered input much more smoothly. The research advances certainly don’t fully replicate human-level discourse yet by any means – likely won’t for some time still. But voice assistants are undoubtedly progressing rapidly from only handling singular, independent transactional commands to managing extended, interrupting conversations with mixed-initiative.

Scaling Knowledge Bases for Depth of Understanding

While pre-trained machine learning models handle a wide variety of verbal expressions rather well, understanding things on a deeper level requires significant knowledge of how the real world works. With broad vocabulary and common sense fundamentals, voice assistants today can sustain relatively surface-level conversational exchanges before hitting dead ends.

However, its softness becomes apparent when users make precise, nuanced queries or delve into a specialty subject area new to the bot. To expand understanding, voice assistants integrate structured knowledge graphs like Google’s Knowledge Vault. These knowledge bases capture factual information about real-world entities, concepts, and general truths used to reliably interpret queries.

Integrated reasoning layers help infer indirect connections by traversing these graphs to satisfy users’ curiosity by providing likely answers even on unfamiliar topics. Of course, effectively codifying humanity’s collective wisdom, knowledge, and problem-solving skills into natural language processing remains a monumentally difficult and long-term endeavor. However, the ongoing development of vast structured knowledge resources and logic inference engines integrated with conversational assistants is rapidly reducing brittleness while extending feasible subjects.

Empowering Assistants to Learn Autonomously 

We’ve explored manual methods to impart conversational assistants with natural language processing, whether through pre-training language models on human dialogue or engineering knowledge graphs covering concepts. However, an intriguing alternative path to mastery that more closely mirrors human learning is enabling voice assistants to expand expertise independently through autonomous self-guided education online. Understanding machine learning algorithms of modern techniques built on the computational theory of mind constructs provide more lifelike scaffolding for virtual assistants to synthesize new knowledge and relationships without human oversight continuously.

Additional self-supervised objectives motivate unguided learning aligned with core design priorities around accuracy, safety and transparency. For example, correctly answering crowdsourced questions from Wikipedia articles could serve as an ongoing automatically evaluated training mechanism. User feedback offers transparent reinforcement indicators for better aligning the assistant’s responses with expectations over time on a personal level.

Preference learning through two-way conversational questionnaires also allows tuning knowledge areas most relevant to each human dialogue partner. Together, this self-directed comprehension aircraft across populations accumulates into formidable intelligence exceeding what’s possible through top-down constrained pre-programming alone.

Of course, strong control procedures will continue to be required to guarantee that variation does not compromise natural language processing for end users. But responsibly guided autonomous progress compresses timelines towards more capable and relatable voice interactions.

Handling Interruptions During Conversations

As humans, we often handle disruptions and changes in topics well while continuing the main conversation from before. Even when briefly talking about unrelated things, we keep the main story in mind to go back to later. However, voice assistants have struggled to “multi-task” during conversations this way. This problem is very clear when assistants try to handle real-world interruptions like pauses, people talking over each other, quick subject changes or users forgetting what they asked.

Handling Interruptions During Conversations

Without special features to deal with these breaks in the usual back-and-forth flow, assistants get very confused or start the conversation over. New methods with natural language processing are starting to address this issue by helping assistants keep talking about the right topic after disruptions. Things like reservoir computing and trajectory frameworks show the early ability to fill information gaps and sustain the context from before.

Adapting Speech Style Based on Audience

Humans automatically modify our speaking manner, vocabulary levels, and even emotional tone depending on who we’re addressing at any given time – for example, small children versus professional colleagues. We alter word complexity, formality, and analogies used and are more appropriate to listener competence as deduced from visual and verbal clues.

However, most voice assistants have historically spoken in a single static voice with very little distinction across users. Advances in parametric speech synthesis over recent years now enable vocal assistants to automatically detect various audience traits like approximate age, cultural background, expertise etc. and modulate tone accordingly. Auditory models like Google’s Transition-Aware Adaptive Speech Synthesizer can shift pitch, pacing, inflexion and more in real-time based on contextual signals to closely match human listeners.

Assistants can additionally tailor topic framing, vocabulary levels and explanation depth responding to seniors versus teenagers while avoiding potentially insensitive assumptions.

Interpreting Emotions and Non-Verbal Signals  

The most of our exploration focuses on the literal verbal content transmitted during human-bot voice interactions. But reading emotional states, non-verbal signals, micro-expressions and other clues enriches human communication tremendously as well. Infusing voice assistants with natural language processing could unlock tremendous utility.

Frontiers in multimodal signal processing and sensor fusion already show promise in decoding aspects of user disposition and state based on vocal biometric signatures, minute facial changes picked up by device cameras, body language shifts detected etc. Tools like Siri demonstrate basic adaptive empathy while Alexa adopts an excited, supportive or soothing intonation flavoured by the exchange.

Scripts for defusing tensions, nudging unpleasant users, or promoting positive mindsets could help assistants manage interpersonal misunderstandings far more graciously as these feeling recognition and sentiment analysis capabilities evolve. Relatability is also affected by humour detection and generation time. Proactively detecting user displeasure via microexpressions or shortened speech could result in genuine apologies and de-escalation attempts to smooth over unavoidable transactional failures later on.

While sizable challenges around emotion-aware conversational AI development services, rapid learning accelerated by the pandemic mass migration to video calls could see assistants adopting natural language processing of cordial conduct sooner than anticipated.

Supporting Multiple Languages in Conversations

Another conversation advantage humans have is seamlessly switching between languages when we want, assuming others understand. We handle integrating slang terms, clarifying names in other tongues and accommodating bilingual speakers without issue assuming shared range. Traditionally though, bots only worked in one language limiting usefulness across cultures.

Fixing this requires going beyond direct word mappings to universal meaning concepts that apply regardless of vocabulary. Advances in models like mT5 anchor understanding to language-independent thought representations translatable across idioms. By first encoding questions into a general concept space, and then re-expressing appropriately for the target language, these approaches prevent getting lost in translation.

Conclusion

When do you think we’ll realize fully natural language processing voice interactions with assistants? While still a way to go before truly replicating unconstrained human discourse. The rapid advances across contextual understanding, common sense reasoning and open-domain knowledge access put highly dynamic natural language processing voice assistants within reach, likely inside this decade.

What gaps or concerns do you still see hampering the widespread adoption of natural language processing with assistant bots akin to human interactions? When do you predict we might realistically chat to voice assistants as comfortably and openly as family or friends?

We would love to hear your perspectives and thoughts about the natural language processing that powers the future of voice bot conversations in the comments below!

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