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

Leveraging textual content technology fashions to construct more practical, scalable buyer assist 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 had been classifiers, or what may be known as discriminative fashions in machine studying (ML) literature. Nevertheless, lately, generative fashions primarily based on large-scale language fashions are quickly gaining traction and basically altering how ML issues are formulated. Generative fashions can now receive some area information via large-scale pre-training after which produce high-quality textual content — for example answering questions or paraphrasing a bit of content material.

At Airbnb, we’ve closely invested in AI textual content technology fashions in our group assist (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 number of the useful traits of textual content technology fashions that make it match for our merchandise.

Making use of AI fashions in large-scale industrial functions like Airbnb buyer assist just isn’t a straightforward problem. Actual-life functions have many long-tail nook circumstances, may be exhausting to scale, and sometimes grow to be expensive to label the coaching knowledge. There are a number of traits of textual content technology fashions that tackle these challenges and make this selection notably helpful.

The primary enticing trait is the potential to encode area information into the language fashions. As illustrated by Petroni et al. (2019), we will encode area information via large-scale pre-training and switch studying. In conventional ML paradigms, enter issues quite a bit. The mannequin is only a transformation perform from the enter to the output. The mannequin coaching focuses primarily on making ready enter, function engineering, and coaching labels. Whereas for generative fashions, the bottom line is the information encoding. How properly we will design the pre-training and coaching to encode high-quality information into the mannequin — and the way properly we design prompts to induce this information — is much extra crucial. This basically modifications how we clear up conventional issues like classifications, rankings, candidate generations, and so on.

Over the previous a number of years, now we have collected huge quantities of information 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 information about fixing customers’ journey issues. At inference time, we’ve designed immediate enter to generate solutions primarily based straight on the encoded human information. This strategy produced considerably higher outcomes in comparison with conventional classification paradigms. A/B testing confirmed important enterprise metric enchancment in addition to considerably higher consumer expertise.

The second trait of the textual content technology mannequin we’ve discovered enticing is its “unsupervised” nature. Giant-scale industrial use circumstances like Airbnb usually have massive quantities of consumer knowledge. Easy methods to mine useful info and information to coach fashions turns into a problem. First, labeling massive quantities of knowledge by human effort could be very expensive, considerably limiting the coaching knowledge scale we may use. Second, designing good labeling tips 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 plenty of nuanced nook circumstances. It doesn’t scale to depend on human effort to exhaust all of the doable consumer intent definitions.

The unsupervised nature of the textual content technology mannequin permits us to coach fashions with out largely labeling the information. Within the pre-training, with a purpose to discover ways 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 publish.

Lastly, textual content technology fashions transcend the normal 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 may be considered as completely different manifestations of the only, unifying drawback of language modeling. A classification may be formatted as a kind of language mannequin the place the output textual content is the literal string illustration of the courses.

With a view to make the language mannequin unification efficient, a brand new however important position 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 needs to be. Together with the immediate, further pure language annotations, or hints, are additionally extremely useful in additional contextualizing the ML drawback as a language technology activity. The incorporation of prompts has been demonstrated to considerably enhance the standard of language fashions on quite a lot 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 technology mannequin

Now, let’s dive into a number of ways in which textual content technology fashions have been utilized inside Airbnb’s Neighborhood Assist merchandise. We’ll discover three use circumstances — content material advice, real-time agent help, and chatbot paraphrasing.

Our content material advice workflow, powering each Airbnb’s Assist Heart search and the assist content material advice 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 difficulty 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 resolution to introduce prompts, we reworked the traditional binary classification drawback right into a prompt-based language technology drawback. The enter remains 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 count on a binary reply, both “Sure” or “No”, of whether or not the doc could be useful in resolving the problem. We additionally added annotations to supply further hints to the meant roles of the varied components 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 a view to consider the standard of the generative classifier, we used manufacturing site visitors knowledge sampled from the identical distribution because the coaching knowledge. The generative mannequin demonstrated important enhancements in the important thing efficiency metric for assist doc rating, as illustrated within the desk under.

Desk 2.1 Airbnb Assist Content material Advice

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

Equipping brokers with the precise contextual information 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 right solutions persistently and helps them resolve consumer points effectively.

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

  • Is that this message a couple of cancellation?
  • What cancellation motive did this consumer point out?
  • Is that this consumer canceling attributable to a COVID illness?
  • Did this consumer unintentionally guide a reservation?
Determine 3.1 AI-generated advice template

With a view to assist 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 completely different prompts would lead to completely different solutions as proven under. Primarily based on the mannequin’s reply, related templates are then advisable to brokers.

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

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

Desk 3.2 Experiment outcomes for mastermind QA mannequin

As a result of massive dimension of the parameters, we leverage a library, known 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. Subsequently, 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 should not adequate for constructing a profitable AI chatbot. Customers usually select to not interact with the chatbot, irrespective of how good the ML mannequin is. Customers wish to clear up issues shortly, so they’re always making an attempt to evaluate if the bot is knowing their drawback and if it is going to resolve the problem sooner than a human agent. Constructing a paraphrase mannequin, which first rephrases the issue a consumer describes, can provide customers some confidence and make sure that the bot’s understanding is right. This has considerably improved our bot’s engagement price. Beneath is an instance of our chatbot mechanically paraphrasing the consumer’s description.

Determine 4.1 An precise instance of the chatbot paraphrasing a consumer’s description of a fee difficulty

This technique of paraphrasing a consumer’s drawback is used usually by human buyer assist brokers. The most typical sample of that is “I perceive that you just…”. For instance, if the consumer asks if they will cancel the reservation without cost, the agent will reply with, “I perceive that you just wish to cancel and wish to know if we will refund the fee 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 now we have a few years of agent-user communication knowledge, this straightforward heuristic provides us hundreds of thousands of coaching labels without cost.

We examined well-liked sequence-to-sequence transformer mannequin backbones like BART, PEGASUS, T5, and so on, and autoregressive fashions like GPT2, and so on. For our use case, the T5 mannequin produced the perfect efficiency.

As discovered by Huang et al. (2020), probably the most widespread problems with the textual content technology mannequin is that it tends to generate bland, generic, uninformative replies. This was additionally the most important problem we confronted.

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

We tried a number of completely 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 had been too generic. Second, we tried to make use of some rule-based or model-based filters.

Ultimately, we discovered the perfect resolution was to tune the coaching knowledge. To do that, we ran textual content clustering on the coaching goal knowledge primarily based on pre-trained similarity fashions from Sentence-Transformers. As seen within the desk under, the coaching knowledge contained too many generic meaningless replies, which induced the mannequin to do the identical in its output.

Desk 4.2 Prime clusters within the coaching labels

We labeled all clusters which can be 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 technology fashions can now encode area information. This not solely permits them to make the most of the appliance knowledge higher, however permits us to coach fashions in an unsupervised means that helps scale knowledge labeling. This allows many revolutionary methods to sort out widespread challenges in constructing AI merchandise. As demonstrated within the three use circumstances detailed on this publish — content material rating, real-time agent help, and chatbot paraphrasing — the textual content technology fashions enhance our consumer experiences successfully in buyer assist eventualities. We consider that textual content technology fashions are an important new route within the NLP area. They assist Airbnb’s friends and hosts clear up their points extra swiftly and help Assist Ambassadors in attaining higher effectivity and a better decision of the problems at hand. We look ahead to 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 publish content material and all the good strategies. Thanks Joy Zhang, Tina Su, Andy Yasutake for his or her management assist! 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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