PRESTO – A multilingual dataset for parsing life like task-oriented dialogues – Google AI Weblog

Digital assistants are more and more built-in into our every day routines. They may also help with all the things from setting alarms to giving map instructions and might even help folks with disabilities to extra simply handle their houses. As we use these assistants, we’re additionally changing into extra accustomed to utilizing pure language to perform duties that we as soon as did by hand.

One of many greatest challenges in constructing a strong digital assistant is figuring out what a person needs and what info is required to carry out the duty at hand. Within the pure language processing (NLP) literature, that is primarily framed as a task-oriented dialogue parsing activity, the place a given dialogue must be parsed by a system to know the person intent and perform the operation to satisfy that intent. Whereas the tutorial neighborhood has made progress in dealing with task-oriented dialogue because of customized function datasets, akin to MultiWOZ, TOP, SMCalFlow, and many others., progress is proscribed as a result of these datasets lack typical speech phenomena essential for mannequin coaching to optimize language mannequin efficiency. The ensuing fashions usually underperform, resulting in dissatisfaction with assistant interactions. Related speech patterns would possibly embody revisions, disfluencies, code-mixing, and using structured context surrounding the person’s surroundings, which could embody the person’s notes, sensible dwelling units, contact lists, and many others.

Take into account the next dialogue that illustrates a typical occasion when a person must revise their utterance:

A dialogue dialog with a digital assistant that features a person revision.

The digital assistant misunderstands the request and makes an attempt to name the wrong contact. Therefore, the person has to revise their utterance to repair the assistant’s mistake. To parse the final utterance accurately, the assistant would additionally must interpret the particular context of the person — on this case, it will must know that the person had a contact record saved of their telephone that it ought to reference.

One other frequent class of utterance that’s difficult for digital assistants is code-mixing, which happens when the person switches from one language to a different whereas addressing the assistant. Take into account the utterance beneath:

A dialogue denoting code-mixing between English and German.

On this instance, the person switches from English to German, the place “vier Uhr” means “4 o’clock” in German.

In an effort to advance analysis in parsing such life like and sophisticated utterances, we’re launching a new dataset known as PRESTO, a multilingual dataset for parsing life like task-oriented dialogues that features roughly half 1,000,000 life like conversations between folks and digital assistants. The dataset spans six completely different languages and contains a number of conversational phenomena that customers might encounter when utilizing an assistant, together with user-revisions, disfluencies, and code-mixing. The dataset additionally contains surrounding structured context, akin to customers’ contacts and lists related to every instance. The express tagging of assorted phenomena in PRESTO permits us to create completely different take a look at units to individually analyze mannequin efficiency on these speech phenomena. We discover that a few of these phenomena are simpler to mannequin with few-shot examples, whereas others require way more coaching information.

Dataset traits

  1. Conversations by native audio system in six languages
    All conversations in our dataset are offered by native audio system of six languages — English, French, German, Hindi, Japanese, and Spanish. That is in distinction to different datasets, akin to MTOP and MASSIVE, that translate utterances solely from English to different languages, which doesn’t essentially replicate the speech patterns of native audio system in non-English languages.
  2. Structured context
    Customers usually depend on the data saved of their units, akin to notes, contacts, and lists, when interacting with digital assistants. Nonetheless, this context is usually not accessible to the assistant, which can lead to parsing errors when processing person utterances. To deal with this challenge, PRESTO contains three forms of structured context, notes, lists, and contacts, in addition to person utterances and their parses. The lists, notes, and contacts are authored by native audio system of every language throughout information assortment. Having such context permits us to look at how this info can be utilized to enhance efficiency on parsing task-oriented dialog fashions.
    Every instance in PRESTO consists of: Inputs — A person’s digital state (context), a number of person utterances, and the corresponding digital assistant responses (dialogue). Output — The semantic parsing of the final person utterance within the dialogue (parse).
  3. Consumer revisions
    It’s common for a person to revise or right their very own utterances whereas talking to a digital assistant. These revisions occur for quite a lot of causes — the assistant might have made a mistake in understanding the utterance or the person might need modified their thoughts whereas making an utterance. One such instance is within the determine above. Different examples of revisions embody canceling one’s request (‘’Don’t add something.”) or correcting oneself in the identical utterance (“Add bread — no, no wait — add wheat bread to my purchasing record.”). Roughly 27% of all examples in PRESTO have some sort of person revision that’s explicitly labeled within the dataset.
  4. Code-mixing
    As of 2022, roughly 43% of the world’s inhabitants is bilingual. In consequence, many customers swap languages whereas talking to digital assistants. In constructing PRESTO, we requested bilingual information contributors to annotate code-mixed utterances, which amounted to roughly 14% of all utterances within the dataset.
    Examples of Hindi-English, Spanish-English, and German-English code-switched utterances from PRESTO.
  5. Disfluencies
    Disfluencies, like repeated phrases or filler phrases, are ubiquitous in person utterances as a result of spoken nature of the conversations that the digital assistants obtain. Datasets akin to DISFL-QA word the dearth of such phenomena in present NLP literature and contribute in direction of the objective of assuaging that hole. In our work, we embody conversations focusing on this explicit phenomenon throughout all six languages.
    Examples of utterances in English, Japanese, and French with filler phrases or repetitions.

Key findings

We carried out focused experiments to deal with every of the phenomena described above. We ran mT5-based fashions educated utilizing the PRESTO dataset and evaluated them utilizing an actual match between the anticipated parse and the human annotated parse. Under we present the relative efficiency enhancements as we scale the coaching information on every of the focused phenomena — person revisions, disfluencies, and code-mixing.

Ok-shot outcomes on varied linguistic phenomena and the total take a look at set throughout growing coaching information measurement.

The ok-shot outcomes yield the next takeaways:

  1. Zero-shot efficiency on the marked phenomenon is poor, emphasizing the necessity for such utterances within the dataset to enhance efficiency.
  2. Disfluencies and code-mixing have a significantly better zero-shot efficiency than user-revisions (over 40 factors distinction in exact-match accuracy).

We additionally examine the distinction between coaching monolingual and multilingual fashions on the practice set and discover that with fewer information multilingual fashions have a bonus over monolingual fashions, however the hole shrinks as the info measurement is elevated.

Further particulars on information high quality, information assortment methodology, and modeling experiments might be present in our paper.


We created PRESTO, a multilingual dataset for parsing task-oriented dialogues that features life like conversations representing quite a lot of ache factors that customers usually face of their every day conversations with digital assistants which can be missing in present datasets within the NLP neighborhood. PRESTO contains roughly half 1,000,000 utterances which can be contributed by native audio system of six languages — English, French, German, Hindi, Japanese, and Spanish. We created devoted take a look at units to deal with every focused phenomenon — person revisions, disfluencies, code-mixing, and structured context. Our outcomes point out that the zero-shot efficiency is poor when the focused phenomenon is just not included within the coaching set, indicating a necessity for such utterances to enhance efficiency. We discover that person revisions and disfluencies are simpler to mannequin with extra information versus code-mixed utterances, that are more durable to mannequin, even with a excessive variety of examples. With the discharge of this dataset, we open extra questions than we reply and we hope the analysis neighborhood makes progress on utterances which can be extra according to what customers are going through on daily basis.


It was a privilege to collaborate on this work with Waleed Ammar, Siddharth Vashishtha, Motoki Sano, Faiz Surani, Max Chang, HyunJeong Choe, David Greene, Kyle He, Rattima Nitisaroj, Anna Trukhina, Shachi Paul, Pararth Shah, Rushin Shah, and Zhou Yu. We’d additionally wish to thank Tom Small for the animations on this weblog publish. Lastly, an enormous because of all of the professional linguists and information annotators for making this a actuality.

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