Linguistics 6: Computers and Language
Dr. Will Styler - Spring 2022
--- ### Today's Plan - What we've learned - The future of Language and Computers - Your future in NLP --- # What we've learned --- ### We've covered a huge amount of material this quarter - Machine Learning - Acoustic and Articulatory Phonetics - Automatic Speech Recognition - Text-to-Speech Synthesis - Unix - Language Modeling - Morphosyntax - POS Tagging and Dependency Parsing - Lexical Semantics and Word Sense - Semantic Parsing - Event Semantics - Pragmatics - Conversational Interaction --- ### We started the quarter with an example --- ### "Hey Siri, how long to drive to work?" - "Traffic to work is light, so it should take 10 minutes via Voigt drive"
--- ### This is amazing
--- ### ... but we now know how it was done! --- ### What did I say? (Acoustic and Articulatory Phonetics) >
"Hey Siri, how long to drive to work?"
--- ### How did she turn that into text? (Automatic Speech Recognition) >
"Hey Siri, how long to drive to work?"
- What parts of it were most difficult? --- ### What parts of speech are there? >
"Hey Siri, how long to drive to work?"
--- ### How did she figure out what parts of speech were there? (Automatic POS Tagging) >
"Hey Siri, how long to drive to work?"
- How/WRB long/RP to/TO drive/VB to/TO work/NN --- ### What kind of syntactic information did she extract? (Dependency parsing) >
"Hey Siri, how long to drive to work?"
--- ### What word senses did she decide on? (Word Sense Disambiguation) >
"Hey Siri, how long to drive to work?"
- drive.v.01 - 'Operate a vehicle' - workplace.n.01 - 'A place where work is done' --- ### What arguments does that verb expect? (Lexical Semantics) >
"Hey Siri, how long to drive to work?"
- Arg0-PAG: driver (vnrole: 11.5-agent) - Arg1-PPT: vehicle or path (vnrole: 11.5-theme) --- ### How do those arguments map onto the sentence? (Shallow Semantic Parsing) >
"Hey Siri, how long to drive to work?"
--- ### What queries do we form from this? (Information Retrieval) - We need ARGM-TMP of 'drive' when ARGM-GOL == 'work' - ARG0-PAG == $USER - $USERLOC == getlocation($DEVICE) - ARGM-GOL == $WORKLOC == 9500 Gilman Drive 92093 - "Get ($TRAFFICSTATUS,$DRIVETIME,$DRIVEROUTE) for navigate($USERLOC,$WORKLOC,car)" --- ### Response Generation (Language Modeling and Pragmatics and Interaction Design) - Traffic to $ARGM-GOL is $TRAFFICSTATUS, so it should take $DRIVETIME minutes via $DRIVEROUTE - >
"Hey Siri, how long to drive to work?"
"Traffic to work is light, so it should take 10 minutes via Voigt drive"
--- ### Response Playback (Text-to-Speech Synthesis) >
"Hey Siri, how long to drive to work?"
"Traffic to work is light, so it should take 10 minutes via Voigt drive"
---
--- ### This remains an *incredibly* complicated process - ... but you now understand the concepts underlying most of it ---
--- ### ... and in the process, you've now covered many of the principal subfields in human linguistics -
--- ### These systems are not perfect, though - There is much room for improvement --- # The Future of Virtual Assistants and NLP --- ### Better NLP functionality - Improved ASR accuracy - Improved TTS quality - Better automatic parsing and POS tagging - Improvements to Lexical Resources - Richer semantic parses --- ### Greater breadth of language coverage - Duplication and expansion of existing resources for other world languages - Attempts at offering such a service to signed language users - **Improved NLP and Virtual Assistant resources for less wealthy languages** --- ### Reduced 'Brittleness' to variability - Less of a 'happy path' effect - Improved ability to handle a greater diversity of phrasings of existing commands - Improved ability to handle a wider diversity of voices and accents - Improved ability to cope with subadjacency constructions - Improved conversational repair --- ### Better reach - Improved ability to interact with a wider number of systems - Improved integration with existing systems - More 'skills' to accomplish more tasks --- ### More safeguards - This tech can be very good, but also very bad - We need to worry a bit about how to prevent incomplete AI from hurting people ---
--- ### Practical Improvements - Offline functionality for ASR and TTS and basic question-answering - Enhanced privacy protections, allowing deeper learning with little trouble - Improvements to wake-word technology allowing even more fluid access and lower idle power consumption - Increased ubiquity at decreased cost --- ### More senses and sensors - Ability to refer to visual data - "What's that over there?" - Leveraging existing sensor meshes --- ### Aside: This is how humans work - We are all vats of brain matter sitting in a dark hole surrounded by bone - We build our understanding from a mesh of sensor data - The 'real world' is just inference from input data --- ### Deeper Understanding and Better Inference - Improved personalized knowledge to enhance inference on real-world topics - Improved discourse context, to allow this to feel more like a 'relationship' - Improved understanding of the person's actual life --- ### Greater Agency - "The next bus will arrive at Gilman and Eucalyptus in 10 minutes. I'll text Jessica and tell her you're on your way." - "It's time to leave for the airport. I've called a Lyft. Don't forget your passport and glasses, leave your pocketknife at home, and make sure to take your good razor out of your toiletry bag if you're using a carry-on." - "Hey, it's time to see the Dentist. Dr. Pradhan's got availability next Tuesday at 2pm, and so do you. Should I confirm?" - "It's Valentine's Day next week. You should remember to grab a card for Jessica. Would you like me to order some dark red roses?" --- ### Better Interaction - Long term knowledge of preferences and desires - Better knowledge of the people, places, and things in the user's life - Improved conversational latency - More natural-feeling conversation --- ### More dynamic conversation - Once we have the basics down, we can make these conversations more natural - Changes to dialect, word choice, etc - More conversational paths and approaches - Less 'wooden' interactions --- ### Artificial Intelligence - General Artificial Intelligence may solve many of these problems - ... and will certainly improve every aspect of these - Even an artificial idiot solves many of them - AI comes with its own difficulties ---
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--- ... but the simple fact is ... --- # Computers need to get better at human language --- ### This will require advances in computing - Improved machine learning - Improved resource efficiency - Better Digital Signal Processing - Better knowledge representations --- ### ... and advances in Linguistics - Better understanding of speech - Better language modeling - Better models of semantics and semantic inference --- ## This is your job, now --- ### You'll need to prepare yourself --- ### Useful Linguistics Courses - LIGN 101 (Intro to Linguistics) - LIGN 110 (Articulatory Phonetics) - LIGN 121 (Syntax) - LIGN 130 (Semantics) --- ### Useful computational follow-up courses - LIGN 17 (Making and Breaking Codes) - LIGN 165 (Computational Linguistics) - LIGN 167 (Deep Learning for Natural Language Understanding) - LIGN 168 (Computational Speech Processing) - ... and lots of grad classes, coming soon! --- ### Also consider the Computational Social Sciences Minor or MS - Focuses on data-driven problems and work throughout the social sciences - NLP is a part of it, but there's lots more! -
--- ### You have many paths - Academia and Research - Government - Industry --- ### ... but these are exciting times --- First, we had keyboards - Then, mice changed everything - ### Now, we are on the verge of interacting with our computers in the same way we interact with everything else --- ### Oh my!
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Thank you!