Abstract: System and method for displaying a user interface of an evaluation system configured to evaluate predicted answers generated by a machine learning system. For example, the method includes receiving textual data and a predicted answer to a question associated with a text object. The text object includes a structured data field of the textual data. The predicted answer includes a confidence level. The confidence level is determined by a machine learning system. In response to determining the confidence level being larger than or equal to a predetermined confidence threshold, the predicted answer and a reference is stored in a storage for retrieval and display. The reference indicates a location of the text object in the textual data. In response to determining the confidence level being smaller than the predetermined confidence threshold, the question and the text object associated with the question is displayed.

Abstract: Conversational image editing and enhancement techniques are described. For example, an indication of a digital image is received from a user. Aesthetic attribute scores for multiple aesthetic attributes of the image are generated. A computing device then conducts a natural language conversation with the user to edit the digital image. The computing device receives inputs from the user to refine the digital image as the natural language conversation progresses. The computing device generates natural language suggestions to edit the digital image based on the aesthetic attribute scores as part of the natural language conversation. The computing device provides feedback to the user that includes edits to the digital image based on the series of inputs. The computing device also includes as feedback natural language outputs indicating options for additional edits to the digital image based on the series of inputs and the previous edits to the digital image.

Abstract: A method, computer program product, and computer system for translating, using a beam search, a source sentence in a source language into a target sentence in a target language by an iterative process.

Abstract: A direct speech-to-speech translation (S2ST) model includes an encoder configured to receive an input speech representation that to an utterance spoken by a source speaker in a first language and encode the input speech representation into a hidden feature representation. The S2ST model also includes an attention module configured to generate a context vector that attends to the hidden representation encoded by the encoder. The S2ST model also includes a decoder configured to receive the context vector generated by the attention module and predict a phoneme representation that corresponds to a translation of the utterance in a second different language. The S2ST model also includes a synthesizer configured to receive the context vector and the phoneme representation and generate a translated synthesized speech representation that corresponds to a translation of the utterance spoken in the different second language.

Abstract: A voice operation apparatus and a control method thereof that can further improve accuracy of talker identification are provided. Provided is a voice operation apparatus including a talker identification unit that identifies a user as a talker of a voice operation based on voice information and a voice quality model of a user registered in advance, and a voice operation recognition unit that performs voice recognition on the voice information and generates voice operation information, wherein the talker identification unit identifies a talker by using, as auxiliary information, at least one of the voice operation information, position information on a voice operation apparatus, direction information on a talker, distance information on a talker, and time information.

Abstract: A system for extracting speaker information in an ATC transcription and displaying the speaker information on a graphical display unit is provided. The system is configured to: segment a stream of audio received from an ATC and other aircraft into a plurality of chunks; determine, for each chunk, if the speaker is enrolled in an enrolled speaker database; when the speaker is enrolled in the enrolled speaker database, decode the chunk using a speaker-dependent automatic speech recognition (ASR) model and tag the chunk with a permanent name for the speaker; when the speaker is not enrolled in the enrolled speaker database, assign a temporary name for the speaker, tag the chunk with the temporary name, and decode the chunk using a speaker independent speech recognition model; format the decoded chunk as text; and signal the graphical display unit to display the formatted text along with an identity for the speaker.

Abstract: The present invention provides a method and a system utilizing an AI entity for confirming an agreement has been entered between a first entity and a second entity during a verbal communication, capturing the portions of the communication that constitute the elements of an agreement and storing the portions for later verification of the agreement.

Abstract: Disclosed embodiments provide techniques to identify the in-context meanings of natural language in order to decipher the evolution or creation of new vocabulary words and create a more holistic user experience. Thus, disclosed embodiments improve the technical field of digital content comprehension. In embodiments, machine learning is used to identify sentiment of text, perform entity detection to determine topics of text, and/or perform image analysis on images used in digital content. Words, symbols, and images that are determined to be potentially unfamiliar to a user are augmented with a supplemental definition indication. Invoking the supplemental definition indication enables rendering of additional definition information for the user. This serves to accelerate understanding of digital content such as webpages and social media posts.

Abstract: A method of phrase extraction for ASR models includes obtaining audio data characterizing an utterance and a corresponding ground-truth transcription of the utterance and modifying the audio data to obfuscate a particular phrase recited in the utterance. The method also includes processing, using a trained ASR model, the modified audio data to generate a predicted transcription of the utterance, and determining whether the predicted transcription includes the particular phrase by comparing the predicted transcription of the utterance to the ground-truth transcription of the utterance. When the predicted transcription includes the particular phrase, the method includes generating an output indicating that the trained ASR model leaked the particular phrase from a training data set used to train the ASR model.

Abstract: A non-transitory computer readable storage medium has instructions executed by a processor to invoke an image processing module to ingest a digital invoice. An evaluation module derives metrics from the digital invoice. A semantic document processing module forms entity extracts from the digital invoice, where each entity extract from the digital invoice has a potential mapping to a trained machine learning model element. An entity extraction correction module overrides the potential mapping to the trained machine learning model element when user feedback from a similar entity extract from a previously processed digital invoice exists to produce a processed digital invoice with a user feedback element inconsistent with the potential mapping to the trained machine learning model element. The processed digital invoice is delivered to an accounting module for final disposition of the digital invoice.

Abstract: The disclosure discloses a method for generating a conversation, an electronic device, and a storage medium. The detailed implementation includes: obtaining a current conversation and historical conversations of the current conversation; selecting multiple reference historical conversations from the historical conversations and adding the multiple reference historical conversations to a temporary conversation set; and generating reply information of the current conversation based on the current conversation and the temporary conversation set.

Abstract: Various embodiments include methods and devices for implementing automatic grammar augmentation for improving voice command recognition accuracy in systems with a small footprint acoustic model. Alternative expressions that may capture acoustic model decoding variations may be added to a grammar set. An acoustic model-specific statistical pronunciation dictionary may be derived by running the acoustic model through a large general speech dataset and constructing a command-specific candidate set containing potential grammar expressions. Greedy based and cross-entropy-method (CEM) based algorithms may be utilized to search the candidate set for augmentations with improved recognition accuracy.

Abstract: Techniques for performing multi-stage entity resolution (ER) processing are described. A system may determine a portion of a user input corresponding to an entity name, and may request an entity provider component to perform a search to determine one or more entities corresponding to the entity name. The preliminary search results may be sent to a skill selection component for processing, while the entity provider component performs a complete search to determine entities corresponding to the entity name. A selected skill component may request the complete search results to perform its processing, including determining an output responsive to the user input.

Abstract: The present disclosure discloses a context-based multi-turn dialogue method.

Abstract: A method and system for identifying the beginning and ending of songs via a machine learning analysis. A machine learning model analyzes streaming audio (such as a radio broadcast) in overlapping, 3-second samples. Each sample is labeled into groups such as “song,” “talk,” “commercial” and “transition.” Based on the location of the transition samples, an exact second a given song begins and ends in the audio stream is derivable. The model further identifies when two songs shift between one another.

Abstract: Methods and systems for multimodal conversational dialogue. The multimodal conversational dialogue system includes multiple sensors to detect multimodal inputs from a user. The multimodal conversational dialogue system includes a multimodal sematic parser that performs semantic parsing and multimodal fusion of the multimodal inputs to determine a goal of the user. The multimodal conversational dialogue system includes a dialogue manager that generates a dialogue with the user in real-time. The dialogue includes system-generated utterances that are used to conduct a conversation between the user and the multimodal conversational dialogue system.

Abstract: A machine translation system, a ChatOps system, a method for a context-aware language machine identification, and computer program product. One embodiment of the machine translation system may include a density calculator. The density calculator may be adapted to calculate a part of speech (POS) density for a plurality of word tokens in an input text, calculate a knowledge density for the plurality of word tokens, and calculate an information density for the plurality of word tokens using the POS density and the knowledge density. In some embodiments, the machine translation system may further comprise a sememe attacher and a context translator.

Abstract: A sound pickup device includes a plurality of microphone elements, a sensitivity correcting unit that corrects a difference in sensitivity among the microphone elements by multiplying an output signal of each of the microphone elements by a gain. The sound pickup device also includes a target sound detecting unit that detects a voice of a speaker as a target sound, a sensitivity correction control unit that controls the gain based on a result of detecting the target sound, and a directivity synthesizing unit that picks up the target sound in a boosted manner using the output signals from the microphone elements of which difference in sensitivity is corrected. The sensitivity correction control unit updates the gain based on the output signals from the microphone elements if the voice of the speaker is detected and does not update the gain if the voice of the speaker is not detected.

Abstract: From metadata of a corpus of natural language text documents, a relativity matrix is constructed, a row-column intersection in the relativity matrix corresponding to a relationship between two instances of a type of metadata. An encoder model is trained, generating a trained encoder model, to compute an embedding corresponding to a token of a natural language text document within the corpus and the relativity matrix, the encoder model comprising a first encoder layer, the first encoder layer comprising a token embedding portion, a relativity embedding portion, a token self-attention portion, a metadata self-attention portion, and a fusion portion, the training comprising adjusting a set of parameters of the encoder model.

Abstract: Techniques for sending prompt data related to content output on a voice-controlled device are described. In an example, a computer system receives request for audio output at a user device. The computer system determines a recommendation for content. The computer system also generates customization data for prompt data based on one or more user features, context features, metadata features, and a history of customization data. The prompt data includes the customization data and an acknowledgement associated with the request. The computer system sends the prompt data to the user device.