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How AI Textual content Technology Fashions Are Reshaping Buyer Assist at Airbnb | by Gavin Li | The Airbnb Tech Weblog

11 min read

Leveraging textual content era fashions to construct simpler, scalable buyer help merchandise.

Gavin Li, Mia Zhao and Zhenyu Zhao

One of many fastest-growing areas in fashionable Synthetic Intelligence (AI) is AI text generation models. Because the title suggests, these fashions generate pure language. Beforehand, most industrial pure language processing (NLP) fashions have been classifiers, or what is perhaps referred to as discriminative fashions in machine studying (ML) literature. Nevertheless, in recent times, generative fashions based mostly on large-scale language fashions are quickly gaining traction and basically altering how ML issues are formulated. Generative fashions can now receive some area data by large-scale pre-training after which produce high-quality textual content — as an example answering questions or paraphrasing a chunk of content material.

At Airbnb, we’ve closely invested in AI textual content era fashions in our neighborhood help (CS) merchandise, which has enabled many new capabilities and use circumstances. This text will talk about three of those use circumstances intimately. Nevertheless, first let’s discuss a few of the useful traits of textual content era fashions that make it an excellent match for our merchandise.

Making use of AI fashions in large-scale industrial purposes like Airbnb buyer help shouldn’t be a straightforward problem. Actual-life purposes have many long-tail nook circumstances, might be onerous to scale, and infrequently develop into pricey to label the coaching knowledge. There are a number of traits of textual content era fashions that deal with these challenges and make this feature significantly helpful.

The primary engaging trait is the aptitude to encode area data into the language fashions. As illustrated by Petroni et al. (2019), we will encode area data by large-scale pre-training and switch studying. In conventional ML paradigms, enter issues rather a lot. The mannequin is only a transformation operate from the enter to the output. The mannequin coaching focuses primarily on getting ready enter, function engineering, and coaching labels. Whereas for generative fashions, the bottom line is the data encoding. How nicely we will design the pre-training and coaching to encode high-quality data into the mannequin — and the way nicely we design prompts to induce this data — is much extra vital. This basically adjustments how we resolve conventional issues like classifications, rankings, candidate generations, and many others.

Over the previous a number of years, we’ve got gathered huge quantities of data of our human brokers providing assist to our friends and hosts at Airbnb. We’ve then used this knowledge to design large-scale pre-training and coaching to encode data about fixing customers’ journey issues. At inference time, we’ve designed immediate enter to generate solutions based mostly straight on the encoded human data. This strategy produced considerably higher outcomes in comparison with conventional classification paradigms. A/B testing confirmed vital enterprise metric enchancment in addition to considerably higher consumer expertise.

The second trait of the textual content era mannequin we’ve discovered engaging is its “unsupervised” nature. Giant-scale industrial use circumstances like Airbnb usually have giant quantities of consumer knowledge. Easy methods to mine useful info and data to coach fashions turns into a problem. First, labeling giant quantities of knowledge by human effort could be very pricey, considerably limiting the coaching knowledge scale we may use. Second, designing good labeling pointers and a complete label taxonomy of consumer points and intents is difficult as a result of real-life issues usually have long-tail distribution and many nuanced nook circumstances. It doesn’t scale to depend on human effort to exhaust all of the potential consumer intent definitions.

The unsupervised nature of the textual content era mannequin permits us to coach fashions with out largely labeling the information. Within the pre-training, as a way to learn to predict the goal labels, the mannequin is compelled to first acquire a sure understanding about the issue taxonomy. Primarily the mannequin is doing a little knowledge labeling design for us internally and implicitly. This solves the scalability points in relation to intent taxonomy design and price of labeling, and subsequently opens up many new alternatives. We’ll see some examples of this after we dive into use circumstances later on this put up.

Lastly, textual content era fashions transcend the standard boundaries of ML drawback formulations Over the previous few years, researchers have realized that the additional dense layers in autoencoding fashions could also be unnatural, counterproductive, and restrictive. In actual fact, the entire typical machine studying duties and drawback formulations might be seen as totally different manifestations of the only, unifying drawback of language modeling. A classification might be formatted as a kind of language mannequin the place the output textual content is the literal string illustration of the lessons.

With the intention to make the language mannequin unification efficient, a brand new however important function is launched: the immediate. A immediate is a brief piece of textual instruction that informs the mannequin of the duty at hand and units the expectation for what the format and content material of the output ought to be. Together with the immediate, extra pure language annotations, or hints, are additionally extremely useful in additional contextualizing the ML drawback as a language era activity. The incorporation of prompts has been demonstrated to considerably enhance the standard of language fashions on a wide range of duties. The determine under illustrates the anatomy of a high-quality enter textual content for common generative modeling.

Determine 1.1 An instance of the immediate and enter function design of our textual content era mannequin

Now, let’s dive into just a few ways in which textual content era fashions have been utilized inside Airbnb’s Group Assist merchandise. We’ll discover three use circumstances — content material suggestion, real-time agent help, and chatbot paraphrasing.

Our content material suggestion workflow, powering each Airbnb’s Assist Heart search and the help content material suggestion in our Helpbot, makes use of pointwise rating to find out the order of the paperwork customers obtain, as proven in Determine 2.1. This pointwise ranker takes the textual illustration of two items of enter — the present consumer’s problem description and the candidate doc, within the type of its title, abstract, and key phrases. It then computes a relevance rating between the outline and the doc, which is used for rating. Previous to 2022, this pointwise ranker had been applied utilizing the XLMRoBERTa, nonetheless we’ll see shortly why we’ve switched to the MT5 mannequin.

Determine 2.1 How we utilized encoder-only structure with an arbitrary classification head to carry out pointwise doc rating

Following the design determination to introduce prompts, we reworked the traditional binary classification drawback right into a prompt-based language era drawback. The enter continues to be derived from each the problem description and the candidate doc’s textual illustration. Nevertheless, we contextualize the enter by prepending a immediate to the outline that informs the mannequin that we anticipate a binary reply, both “Sure” or “No”, of whether or not the doc can be useful in resolving the problem. We additionally added annotations to offer additional hints to the meant roles of the assorted elements of the enter textual content, as illustrated within the determine under. To allow personalization, we expanded the problem description enter with textual representations of the consumer and their reservation info.

Determine 2.2. How we leveraged an encoder-decoder structure with a pure language output to function a pointwise ranker

We fine-tuned the MT5 mannequin on the duty described above. With the intention to consider the standard of the generative classifier, we used manufacturing visitors knowledge sampled from the identical distribution because the coaching knowledge. The generative mannequin demonstrated vital enhancements in the important thing efficiency metric for help doc rating, as illustrated within the desk under.

Desk 2.1 Airbnb Assist Content material Suggestion

As well as, we additionally examined the generative mannequin in a web-based A/B experiment, integrating the mannequin into Airbnb’s Assist Heart, which has tens of millions of lively customers. The profitable experimentation outcomes led to the identical conclusion — the generative mannequin recommends paperwork with considerably larger relevance as compared with the classification-based baseline mannequin.

Equipping brokers with the best contextual data and highly effective instruments results in higher experiences for our clients. So we offer our brokers with just-in-time steerage, which directs them to the proper solutions constantly and helps them resolve consumer points effectively.

For instance, by agent-user conversations, advised templates are displayed to help brokers in drawback fixing. To verify our recommendations are enforced inside CS coverage, suggestion templates are gated by a mixture of API checks and mannequin intent checks. This mannequin must reply inquiries to seize consumer intents comparable to:

  • Is that this message a few cancellation?
  • What cancellation purpose did this consumer point out?
  • Is that this consumer canceling as a consequence of a COVID illness?
  • Did this consumer by accident e-book a reservation?
Determine 3.1 AI-generated suggestion template

With the intention to help many granular intent checks, we developed a mastermind Query-Answering (QA) mannequin, aiming to assist reply all associated questions. This QA mannequin was developed utilizing the generative mannequin structure talked about above. We concatenate a number of rounds of user-agent conversations to leverage chat historical past as enter textual content after which ask the immediate we care about on the time limit of serving.

Prompts are naturally aligned with the identical questions we ask people to annotate. Barely totally different prompts would end in totally different solutions as proven under. Based mostly on the mannequin’s reply, related templates are then really useful to brokers.

Desk 3.1 Immediate design for mastermind QA mannequin
Determine 2.2 Mastermind QA mannequin structure

We leveraged spine fashions comparable to t5-base and Narrativa and did experimentations on numerous coaching dataset compositions together with annotation-based knowledge and logging-based knowledge with extra post-processing. Annotation datasets often have larger precision, decrease protection, and extra constant noise, whereas logging datasets have decrease precision, larger case protection, and extra random noises. We discovered that combining these two datasets collectively yielded one of the best efficiency.

Desk 3.2 Experiment outcomes for mastermind QA mannequin

As a result of giant dimension of the parameters, we leverage a library, referred to as DeepSpeed, to coach the generative mannequin utilizing multi GPU cores. DeepSpeed helps to hurry up the coaching course of from weeks to days. That being stated, it usually requires longer for hyperparameter tunings. Due to this fact, experiments are required with smaller datasets to get a greater route on parameter settings. In manufacturing, on-line testing with actual CS ambassadors confirmed a big engagement price enchancment.

Correct intent detection, slot filling, and efficient options aren’t adequate for constructing a profitable AI chatbot. Customers usually select to not have interaction with the chatbot, regardless of how good the ML mannequin is. Customers need to resolve issues shortly, so they’re continually attempting to evaluate if the bot is knowing their drawback and if it’s going to resolve the problem sooner than a human agent. Constructing a paraphrase mannequin, which first rephrases the issue a consumer describes, may give customers some confidence and make sure that the bot’s understanding is appropriate. This has considerably improved our bot’s engagement price. Under is an instance of our chatbot robotically paraphrasing the consumer’s description.

Determine 4.1 An precise instance of the chatbot paraphrasing a consumer’s description of a cost problem

This methodology of paraphrasing a consumer’s drawback is used usually by human buyer help brokers. The commonest sample of that is “I perceive that you just…”. For instance, if the consumer asks if they will cancel the reservation free of charge, the agent will reply with, “I perceive that you just need to cancel and want to know if we will refund the cost in full.” We constructed a easy template to extract all of the conversations the place an agent’s reply begins with that key phrase. As a result of we’ve got a few years of agent-user communication knowledge, this easy heuristic offers us tens of millions of coaching labels free of charge.

We examined widespread sequence-to-sequence transformer mannequin backbones like BART, PEGASUS, T5, and many others, and autoregressive fashions like GPT2, and many others. For our use case, the T5 mannequin produced one of the best efficiency.

As discovered by Huang et al. (2020), one of the vital frequent problems with the textual content era mannequin is that it tends to generate bland, generic, uninformative replies. This was additionally the key problem we confronted.

For instance, the mannequin outputs the identical reply for a lot of totally different inputs: “I perceive that you’ve some points together with your reservation.” Although appropriate, that is too generic to be helpful.

We tried a number of totally different options. First, we tried to construct a backward mannequin to foretell P(Supply|goal), as launched by Zhang et al. (2020), and use it as a reranking mannequin to filter out outcomes that have been too generic. Second, we tried to make use of some rule-based or model-based filters.

In the long run, we discovered one of the best answer was to tune the coaching knowledge. To do that, we ran textual content clustering on the coaching goal knowledge based mostly on pre-trained similarity fashions from Sentence-Transformers. As seen within the desk under, the coaching knowledge contained too many generic meaningless replies, which prompted the mannequin to do the identical in its output.

Desk 4.2 High clusters within the coaching labels

We labeled all clusters which are too generic and used Sentence-Transformers to filter them out from the coaching knowledge. This strategy labored considerably higher and gave us a high-quality mannequin to place into manufacturing.

With the quick progress of large-scale pre-training-based transformer fashions, the textual content era fashions can now encode area data. This not solely permits them to make the most of the appliance knowledge higher, however permits us to coach fashions in an unsupervised approach that helps scale knowledge labeling. This permits many modern methods to deal with frequent challenges in constructing AI merchandise. As demonstrated within the three use circumstances detailed on this put up — content material rating, real-time agent help, and chatbot paraphrasing — the textual content era fashions enhance our consumer experiences successfully in buyer help eventualities. We consider that textual content era fashions are a vital new route within the NLP area. They assist Airbnb’s friends and hosts resolve their points extra swiftly and help Assist Ambassadors in attaining higher effectivity and a better decision of the problems at hand. We stay up for persevering with to take a position actively on this space.

Thanks Weiping Pen, Xin Liu, Mukund Narasimhan, Joy Zhang, Tina Su, Andy Yasutake for reviewing and sprucing the weblog put up content material and all the nice recommendations. Thanks Joy Zhang, Tina Su, Andy Yasutake for his or her management help! Thanks Elaine Liu for constructing the paraphrase end-to-end product, operating the experiments, and launching. Thanks to our shut PM companions, Cassie Cao and Jerry Hong, for his or her PM experience. This work couldn’t have occurred with out their efforts.

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