How Alexa is learning to converse more naturally

To handle more-natural spoken interactions, Alexa must track references through several rounds of conversation. If, for instance, a customer says, “How far is it to Redmond?” and after the answer follows up by saying, “Find good Indian restaurants there”, Alexa should be able to infer that “there” refers to Redmond.

We call the task of reference tracking “context carryover,” and it’s a capability that is currently being phased in to the Alexa experience. At this year’s Interspeech, the largest conference on spoken-language understanding, my colleagues and I will present a paper titled “Contextual Slot Carryover for Disparate Schemas,” which describes our solution to the problem of slot carryover, a crucial aspect of context carryover.

Today, Alexa analyzes the semantic content of utterances according to the categories domain, intent, and slot. “Domain” describes the type of application — or “skill” — that the utterance should invoke; for instance, mapping skills should answer questions about geographic distance. “Intent” describes the particular function the skill should execute, such as measuring driving distance. And “slots” are variables that the function acts upon, such as point of origin and destination.

Answering successive questions in a natural conversation will often require Alexa to invoke different skills, which makes context carryover extremely hard. The mapping skill, for instance, might use the slot “Town” to describe travel destinations, whereas the Restaurants skill might use the slot “City” to describe the geographic area in which it is performing a search. In our Interspeech paper, we describe a neural-network approach that automatically learns how to map the slots used by one skill to those used by another.

Slot mapping
Different Alexa intents (weather, search, directions) might assign the same data (San Francisco) to different slots (WeatherLocation, City, Town). Tracking references by carrying slots over from one intent to the next is crucial for maintaining a coherent conversation.

We make an important distinction between our approach and conventional dialogue state tracking, which maintains a probability distribution across all possible values that a given target slot can take on. Our system, by contrast, (i) cares only about slot values mentioned in context and (ii) makes independent decisions about the probability that any given carryover decision is the correct one. This helps us scale to the large number of slots across all Alexa skills.

Our system uses an encoder-decoder model, which means that it divides the neural network into two components. The first component — the encoder — receives vectors representing various features of the input data and outputs a single summary vector. The second component — the decoder — receives the summary vector and outputs a confidence score, which represents the likelihood that a candidate slot is the correct one. Both components of the system are trained together, so the encoder learns to produce summary vectors that are particularly useful for candidate scoring. Below, we describe this architecture in more detail.

During training, our system uses a set of slot names (such as “Town”) and all their associated values (such as “Redmond”) to create an “embedding” for each slot name. Embedding is a technique for representing strings of words as points in a geometric space, such that semantically related strings are grouped together. Typically, it’s based on the frequency with which words co-occur with other words.

When the system is in use, we use proximity in embedding space to generate a list of candidate mappings between every slot encountered in the conversation so far and the slots available in the currently invoked skill. Each of these candidates is then fed into the encoder, along with other features, such as the recent history of the customer’s utterances, the recent history of Alexa’s responses, and the inferred intent of the customer’s most recent utterance.

The utterance histories pass through layers of the encoder known as long short-term memory (LSTM) encoders. LSTMs are neural-network layers that can account for the sequencing of data, so they preserve the information inherent in utterances’ word order. Each LSTM also has an associated word attention mechanism. During training, the word attention mechanisms learn which words in an utterance are particularly useful for assessing a candidate slot-mapping. We also add a separate attention mechanism to help the decoder decide whether to focus on user utterances or Alexa utterances.

Our system architecture
Our system architecture

In the encoder, the utterance histories are combined into a single output vector, but the candidate slot-mapping and the intent inference are encoded separately (as is a “recency encoding” that describes the conversational distance between the slot candidate and the slot whose value it’s inheriting). The outputs of the encoder then pass to the decoder, which consists of several densely interconnected network layers. Finally, the decoder outputs a decision about whether to carry over the slot or not.

In the paper we compare the performance of the network with and without the attention mechanisms to that of a strong rule-based system for slot mapping, which consists of hand-coded rules such as, If the initial slot type is “City,” and the follow-up utterance includes the word “there,” then the value of City should be carried over.

Generally, the attention mechanisms offered slight improvements in system performance. The rule-based system had a significantly lower recall than our model, meaning that it missed many carryovers that our model correctly deduced. Overall, according to the F1 score, which combines recall and precision (a measure of the false-positive rate), our system outperformed the rule-based system by roughly 9 percent.

Paper: "Contextual Slot Carryover for Disparate Schemas"

Alexa science

Acknowledgments: Hancheng Ge, Lambert Mathias, Ruhi Sarikaya

About the Author
Arpit Gupta is a speech scientist in the Alexa AI group.
About the Author
Chetan Naik is a machine-learning scientist with the Alexa AI organization.

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Payments Security team is looking for an Applied Scientist to apply formal verification, program analysis, and constraint-solving to prove the correctness of critical systems. In this role, you will work closely with internal security teams to design and build formal verification systems that continuously assess safety and security. You will build on top of existing formal verification tools developed by AWS and develop new methods to apply those tools at scale. You will need to be innovative, entrepreneurial, and adaptable. We like to move fast, experiment, iterate and then scale quickly, thoughtfully balancing speed and quality.
US, WA, Bellevue
Amazon aims to exceed the expectations of our customers by ensuring that their orders, no matter how large or small, are delivered as quickly, accurately, and cost effectively as possible. To meet this goal, Amazon has invested in Amazon Logistics, a world class last mile operation. We are looking for a dynamic, resourceful, and organized Sr. Applied Scientist within Amazon Logistics’ Amazon Flex organization to develop the right data-driven solutions to support the most critical components of this rapidly scaling operation.As part of the Driver Science team, you’ll partner closely with Science, Product, and Program teams to design of new and innovative solutions to organizational and business problems, using statistical best practices to justify design decisions using data. You will be expected to be a subject matter expert in quantitative analysis and to communicate highly complex findings to multiple audiences. You may come from one of many different types of quantitative backgrounds, but you must be an expert in big data, machine learning, and productionalization. You will be working closely with Tech teams but will be responsible to helping rapidly push models into production and measuring business impact. You are a thought leader, creating scientific strategies and visions to solve ambiguous problems.Key Responsibilities· Execute global research initiatives· Conduct, direct, and coordinate all phases of research projects, demonstrating skill in all stages of the analysis process, including defining key research questions, recommending measures, working with multiple data sources, evaluating methodology and design, executing analysis plans, interpreting and communicating results· Share deep knowledge in machine learning to our problem space.· Work in an ambiguous environment