Abstract: Techniques for extracting key information from a document using machine-learning models in a chatbot system is disclosed herein. In one particular aspect, a method is provided that includes receiving a set of data, which includes key fields, within a document at a data processing system that includes a table detection module, a key information extraction module, and a table extraction module. Text information and corresponding location data are extracted via optical character recognition. The table detection module detects whether one or more tables are present in the document and, if applicable, a location of each of the tables. The key information extraction module extracts text from the key fields. The table extraction module extracts each of the tables based on input from the optical character recognition and the table detection module. Extraction results include the text from the key fields and each of the tables can be output.

Abstract: In some aspects, a computing device may receive, at a data processing system, a set of utterances for training or inferencing with a named entity recognizer to assign a label to each token piece from the set of utterances. The computing device may determine a length of each utterance in the set and when the length of the utterance exceeds a pre-determined threshold of token pieces: dividing the utterance into a plurality of overlapping chunks of token pieces; assigning a label together with a confidence score for each token piece in a chunk; determining a final label and an associated confidence score for each chunk of token pieces by merging two confidence scores; determining a final annotated label for the utterance based at least on the merging the two confidence scores; and storing the final annotated label in a memory.

Abstract: Techniques for using logit values for classifying utterances and messages input to chatbot systems in natural language processing. A method can include a chatbot system receiving an utterance generated by a user interacting with the chatbot system. The chatbot system can input the utterance into a machine-learning model including a set of binary classifiers. Each binary classifier of the set of binary classifiers can be associated with a modified logit function. The method can also include the machine-learning model using the modified logit function to generate a set of distance-based logit values for the utterance. The method can also include the machine-learning model applying an enhanced activation function to the set of distance-based logit values to generate a predicted output. The method can also include the chatbot system classifying, based on the predicted output, the utterance as being associated with the particular class.

Abstract: A data corpus is partitioned into text strings for header classification. A group characteristic is computed for a text string, and whether the group characteristic satisfies a group characteristic criterion is determined. The text string may be disqualified from header classification if the group characteristic criterion is not satisfied, or one or more font characteristics may be determined for the text string if the group characteristic criterion is satisfied. A font characteristic that meets one or more prevalence criteria may be identified and evaluated to determine whether the font characteristic meets at least one font characteristic criterion. The text string may be disqualified from header classification if the font characteristic criterion is not satisfied, or if the font characteristic meets the font characteristic criterion, the text string is classified as a header, and tagged content is generated by applying a header tag to the text string.

Abstract: A method includes receiving an indication of a first coverage value corresponding to a desired overlap between a dataset of natural language phrases and a training dataset for training a machine learning model; determining a second coverage value corresponding to a measured overlap between the dataset of natural language phrases and the training dataset; determining a coverage delta value based on a comparison between the first coverage value and the second coverage value; modifying, based on the coverage delta value, the dataset of natural language phrases; and processing, utilizing a machine learning model including the modified dataset of natural language phrases, an input dataset including a set of input features. The machine learning model processes the input dataset based at least in part on the dataset of natural language phrases to generate an output dataset.

Abstract: Techniques for keyword data augmentation for training chatbot systems in natural language processing. In one particular aspect, a method is provided that includes receiving a training set of utterances for training a machine-learning model to identify one or more intents for one or more utterances, augmenting the training set of utterances with out-of-domain (OOD) examples. The augmenting includes: identifying keywords within utterances of the training set of utterances, generating a set of OOD examples with the identified keywords, filtering out OOD examples from the set of OOD examples that have a context substantially similar to context of the utterances of the training set of utterances, and incorporating the set of OOD examples without the filtered OOD examples into the training set of utterances to generate an augmented training set of utterances. Thereafter, the machine-learning model is trained using the augmented training set of utterances.

Abstract: Techniques are disclosed for systems including techniques for multi-feature balancing for natural langue processors. In an embodiment, a method includes receiving a natural language query to be processed by a machine learning model, the machine learning model utilizing a dataset of natural language phrases for processing natural language queries, determining, based on the machine learning model and the natural language query, a feature dropout value, generating, and based on the natural language query, one or more contextual features and one or more expressional features that may be input to the machine learning model, modifying at least one or the one or more contextual features and the one or more expressional features based on the feature dropout value to generate a set of input features for the machine learning model, and processing the set of input features to cause generating an output dataset for corresponding to the natural language query.

Abstract: Techniques for multi-layer training of a machine learning model are disclosed. A system pre-trains a machine learning model on training data obtained from unlabeled document graph data by executing unsupervised pre-training tasks on the unlabeled document graph data to generate a labeled pre-training data set. The system modifies document graphs to change attributes of nodes in the document graphs. The system pre-trains the machine learning model with a data set including the modified document graphs and un-modified document graphs to generate prediction associated with the modifications to the document graphs. Subsequent to pre-training, the system fine-tunes the machine learning model with a set of labeled training data to generate predictions associated with a specific attribute of a document graph.

Abstract: Techniques are disclosed for systems including techniques for multi-factor modelling for training and utilizing chatbot systems for natural language processing. In an embodiment, a method includes receiving a set of utterance data corresponding to a natural language-based query, determining one or more intents for the chatbot corresponds to a possible context for the natural language-based query and associated with a skill for the chatbot, generating one or more intent classification datasets, each intent classification dataset associated with a probability that the natural language query corresponds to an intent of the one or more intents, generating one or more transformed datasets each corresponding to a skill of one or more skills, determining a first skill of the one or more skills based on the one or more transformed datasets and processing, based on the determined first skill, the set of utterance data to resolve the natural language-based query.

Abstract: The present disclosure relates to techniques for identifying out-of-domain utterances.

Abstract: Techniques for generating formatting tags for textual content obtained from a source electronic document are disclosed. A system parses a digital file to obtain information about characters in an electronic document. The system applies tags to text generated based on the textual content of the electronic document by creating segments of textually-consecutive characters and applying corresponding text formatting style tags to the segments. The system further identifies segments of text overlapping bounding boxes in the electronic document. The system generates textual content including a segment of text and a corresponding hyperlink associated with the segment of text. The system further generates textual content by selectively applying line breaks from the source electronic document in the textual content.

Abstract: Techniques for identifying content in key-value pairs of documents using a graph neural network (GNN) are disclosed. A system trains a GNN to identify key-value pair groupings in documents. The GNN classifies nodes in document graphs as key-type nodes and answer-type nodes. The GNN also classifies edges connecting nodes in the document graphs for keeping in the document graph or removing from the document graph. The resulting document graph includes key-value groupings in the document. Upon identifying content matching a query, a system returns as a query response content from among the key-value groupings.

Abstract: A method includes receiving a training set of utterances for training a machine-learning model to identify one or more intents for one or more utterances, and augmenting the training set of utterances with out-of-domain (OOD) examples. The augmenting includes: generating a data set of OOD examples, filtering out OOD examples from the data set of OOD examples, determining a difficulty value for each OOD example remaining within the filtered data set of the OOD examples, and generating augmented batches of utterances including utterances from the training set of utterances and utterances from the filtered data set of the OOD based on the difficulty value for each OOD. Thereafter, the machine-learning model is trained using the augmented batches of utterances in accordance with a curriculum training protocol.

Abstract: Techniques for using enhanced logit values for classifying utterances and messages input to chatbot systems in natural language processing. A method can include a chatbot system receiving an utterance generated by a user interacting with the chatbot system and inputting the utterance into a machine-learning model including a series of network layers. A final network layer of the series of network layers can include a logit function. The machine-learning model can map a first probability for a resolvable class to a first logit value using the logit function. The machine-learning model can map a second probability for a unresolvable class to an enhanced logit value. The method can also include the chatbot system classifying the utterance as the resolvable class or the unresolvable class based on the first logit value and the enhanced logit value.

Abstract: The present disclosure is related to techniques for converting a natural language utterance to a logical form query and deriving a natural language interpretation of the logical form query. The techniques include accessing a Meaning Resource Language (MRL) query and converting the MRL query into a MRL structure including logical form statements. The converting includes extracting operations and associated attributes from the MRL query and generating the logical form statements from the operations and associated attributes. The techniques further include translating each of the logical form statements into a natural language expression based on a grammar data structure that includes a set of rules for translating logical form statements into corresponding natural language expressions, combining the natural language expressions into a single natural language expression, and providing the single natural language expression as an interpretation of the natural language utterance.

Abstract: Techniques for out-of-domain data augmentation for training chatbot systems in natural language processing. In one particular aspect, a method is provided that includes receiving a training set of utterances for training a machine-learning model to identify one or more intents for one or more utterances, and augmenting the training set of utterances with out-of-domain (OOD) examples. The augmenting includes: generating a data set of OOD examples, filtering out OOD examples from the data set of OOD examples, determining a difficulty value for each OOD example remaining within the filtered data set of the OOD examples, and generating augmented batches of utterances comprising utterances from the training set of utterances and utterances from the filtered data set of the OOD based on the difficulty value for each OOD. Thereafter, the machine-learning model is trained using the augmented batches of utterances in accordance with a curriculum training protocol.

Abstract: Techniques for using logit values for classifying utterances and messages input to chatbot systems in natural language processing. A method can include a chatbot system receiving an utterance generated by a user interacting with the chatbot system. The chatbot system can input the utterance into a machine-learning model including a set of binary classifiers. Each binary classifier of the set of binary classifiers can be associated with a modified logit function. The method can also include the machine-learning model using the modified logit function to generate a set of distance-based logit values for the utterance. The method can also include the machine-learning model applying an enhanced activation function to the set of distance-based logit values to generate a predicted output. The method can also include the chatbot system classifying, based on the predicted output, the utterance as being associated with the particular class.

Abstract: The present disclosure relates to techniques for identifying out-of-domain utterances.

Abstract: A data corpus is partitioned into text strings for header classification. A group characteristic is computed for a text string, and whether the group characteristic satisfies a group characteristic criterion is determined. The text string may be disqualified from header classification if the group characteristic criterion is not satisfied, or one or more font characteristics may be determined for the text string if the group characteristic criterion is satisfied. A font characteristic that meets one or more prevalence criteria may be identified and evaluated to determine whether the font characteristic meets at least one font characteristic criterion. The text string may be disqualified from header classification if the font characteristic criterion is not satisfied, or if the font characteristic meets the font characteristic criterion, the text string is classified as a header, and tagged content is generated by applying a header tag to the text string.

Abstract: Obtaining collections of sentences in different languages that are usable for training models in various applications of artificial intelligence is provided. A method is provided that obtains, from text corpus, webpages in a plurality of languages, each of the webpages corresponding to an URL; obtains annotations for each of the webpages based on its URL, to obtain annotated data entries corresponding to the webpages, each of the annotated data entries including a classification label corresponding to a sub-topic of one of a plurality of topics, where each of the plurality of topics includes a corresponding plurality of sub-topics; filters the annotated data entries to obtain topic-specific content in a target language based on the classification labels, the topic-specific content corresponding to one or more sub-topics; performs post-processing on the topic-specific content to obtain result data; and outputs the result data for the topic.