With the power of large pretrained language models, various research works have integrated knowledge into dialogue systems. The traditional techniques treat knowledge as part of the input sequence for the dialogue system, prepending a set of knowledge statements in front of dialogue history. However, such a mechanism forces knowledge sets to be concatenated in an ordered manner, making models implicitly pay imbalanced attention to the sets during training. In this paper, we first investigate how the order of the knowledge set can influence autoregressive dialogue systems’ responses. We conduct experiments on two commonly used dialogue datasets with two types of transformer-based models and find that models view the input knowledge unequally. To this end, we propose a simple and novel technique to alleviate the order effect by modifying the position embeddings of knowledge input in these models. With the proposed position embedding method, the experimental results show that each knowledge statement is uniformly considered to generate responses.

Enriching the quality of early childhood education with interactive math learning at home systems, empowered by recent advances in conversational AI technologies, is slowly becoming a reality. With this motivation, we implement a multimodal dialogue system to support play-based learning experiences at home, guiding kids to master basic math concepts. This work explores Spoken Language Understanding (SLU) pipeline within a task-oriented dialogue system developed for Kid Space, with cascading Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU) components evaluated on our home deployment data with kids going through gamified math learning activities. We validate the advantages of a multi-task architecture for NLU and experiment with a diverse set of pretrained language representations for Intent Recognition and Entity Extraction tasks in the math learning domain. To recognize kids’ speech in realistic home environments, we investigate several ASR systems, including the commercial Google Cloud and the latest open-source Whisper solutions with varying model sizes. We evaluate the SLU pipeline by testing our best-performing NLU models on noisy ASR output to inspect the challenges of understanding children for math learning in authentic homes.

Conversational assistants are ubiquitous among the general population, however, these systems have not had an impact on people with disabilities, or speech and language disorders, for whom basic day-to-day communication and social interaction is a huge struggle. Language model technology can play a huge role in empowering these users and help them interact with others with less effort via interaction support. To enable this population, we build a system that can represent them in a social conversation and generate responses that can be controlled by the users using cues/keywords. We build models that can speed up this communication by suggesting relevant cues in the dialog response context. We also introduce a keyword-loss to lexically constrain the model response output. We present automatic and human evaluation of our cue/keyword predictor and the controllable dialog system to show that our models perform significantly better than models without control. Our evaluation and user study shows that keyword-control on end-to-end response generation models is powerful and can enable and empower users with degenerative disorders to carry out their day-to-day communication.

Intelligent conversational assistants have become an integral part of our lives for performing simple tasks. However, such agents, for example, Google bots, Alexa and others are yet to have any social impact on minority population, for example, for people with neurological disorders and people with speech, language and social communication disorders, sometimes with locked-in states where speaking or typing is a challenge. Language model technologies can be very powerful tools in enabling these users to carry out daily communication and social interactions. In this work, we present a system that users with varied levels of disabilties can use to interact with the world, supported by eye-tracking, mouse controls and an intelligent agent Cue-bot, that can represent the user in a conversation. The agent provides relevant controllable ‘cues’ to generate desirable responses quickly for an ongoing dialog context. In the context of usage of such systems for people with degenerative disorders, we present automatic and human evaluation of our cue/keyword predictor and the controllable dialog system and show that our models perform significantly better than models without control and can also reduce user effort (fewer keystrokes) and speed up communication (typing time) significantly.

Intelligent systems designed for play-based interactions should be contextually aware of the users and their surroundings. Spoken Dialogue Systems (SDS) are critical for these interactive agents to carry out effective goal-oriented communication with users in real-time. For the real-world (i.e., in-the-wild) deployment of such conversational agents, improving the Natural Language Understanding (NLU) module of the goal-oriented SDS pipeline is crucial, especially with limited task-specific datasets. This study explores the potential benefits of a recently proposed transformer-based multi-task NLU architecture, mainly to perform Intent Recognition on small-size domain-specific educational game datasets. The evaluation datasets were collected from children practicing basic math concepts via play-based interactions in game-based learning settings. We investigate the NLU performances on the initial proof-of-concept game datasets versus the real-world deployment datasets and observe anticipated performance drops in-the-wild. We have shown that compared to the more straightforward baseline approaches, Dual Intent and Entity Transformer (DIET) architecture is robust enough to handle real-world data to a large extent for the Intent Recognition task on these domain-specific in-the-wild game datasets.

Contextually aware intelligent agents are often required to understand the users and their surroundings in real-time. Our goal is to build Artificial Intelligence (AI) systems that can assist children in their learning process. Within such complex frameworks, Spoken Dialogue Systems (SDS) are crucial building blocks to handle efficient task-oriented communication with children in game-based learning settings. We are working towards a multimodal dialogue system for younger kids learning basic math concepts. Our focus is on improving the Natural Language Understanding (NLU) module of the task-oriented SDS pipeline with limited datasets. This work explores the potential benefits of data augmentation with paraphrase generation for the NLU models trained on small task-specific datasets. We also investigate the effects of extracting entities for conceivably further data expansion. We have shown that paraphrasing with model-in-the-loop (MITL) strategies using small seed data is a promising approach yielding improved performance results for the Intent Recognition task.

The advances in language-based Artificial Intelligence (AI) technologies applied to build educational applications can present AI for social-good opportunities with a broader positive impact. Across many disciplines, enhancing the quality of mathematics education is crucial in building critical thinking and problem-solving skills at younger ages. Conversational AI systems have started maturing to a point where they could play a significant role in helping students learn fundamental math concepts. This work presents a task-oriented Spoken Dialogue System (SDS) built to support play-based learning of basic math concepts for early childhood education. The system has been evaluated via real-world deployments at school while the students are practicing early math concepts with multimodal interactions. We discuss our efforts to improve the SDS pipeline built for math learning, for which we explore utilizing MathBERT representations for potential enhancement to the Natural Language Understanding (NLU) module. We perform an end-to-end evaluation using real-world deployment outputs from the Automatic Speech Recognition (ASR), Intent Recognition, and Dialogue Manager (DM) components to understand how error propagation affects the overall performance in real-world scenarios.

In this work, we develop a dataset for incremental temporal summarization in a multiparty dialogue. We use crowd-sourcing paradigm with a model-in-loop approach for collecting the summaries and compare the data with the expert summaries. We leverage the question generation paradigm to automatically generate questions from the dialogue, which can be used to validate the user participation and potentially also draw attention of the user towards the contents then need to summarize. We then develop several models for abstractive summary generation in the Incremental temporal scenario. We perform a detailed analysis of the results and show that including the past context into the summary generation yields better summaries.

Building the Natural Language Understanding (NLU) modules of task-oriented Spoken Dialogue Systems (SDS) involves a definition of intents and entities, collection of task-relevant data, annotating the data with intents and entities, and then repeating the same process over and over again for adding any functionality/enhancement to the SDS. In this work, we showcase an Intent Bulk Labeling system where SDS developers can interactively label and augment training data from unlabeled utterance corpora using advanced clustering and visual labeling methods. We extend the Deep Aligned Clustering work with a better backbone BERT model, explore techniques to select the seed data for labeling, and develop a data balancing method using an oversampling technique that utilizes paraphrasing models. We also look at the effect of data augmentation on the clustering process. Our results show that we can achieve over 10% gain in clustering accuracy on some datasets using the combination of the above techniques. Finally, we extract utterance embeddings from the clustering model and plot the data to interactively bulk label the samples, reducing the time and effort for data labeling of the whole dataset significantly.

Our senses individually work in a coordinated fashion to express our emotional intentions. In this work, we experiment with modeling modality-specific sensory signals to attend to our latent multimodal emotional intentions and vice versa expressed via low-rank multimodal fusion and multimodal transformers. The low-rank factorization of multimodal fusion amongst the modalities helps represent approximate multiplicative latent signal interactions. Motivated by the work of (CITATION) and (CITATION), we present our transformer-based cross-fusion architecture without any over-parameterization of the model. The low-rank fusion helps represent the latent signal interactions while the modality-specific attention helps focus on relevant parts of the signal. We present two methods for the Multimodal Sentiment and Emotion Recognition results on CMU-MOSEI, CMU-MOSI, and IEMOCAP datasets and show that our models have lesser parameters, train faster and perform comparably to many larger fusion-based architectures.

Building multimodal dialogue understanding capabilities situated in the in-cabin context is crucial to enhance passenger comfort in autonomous vehicle (AV) interaction systems. To this end, understanding passenger intents from spoken interactions and vehicle vision systems is an important building block for developing contextual and visually grounded conversational agents for AV. Towards this goal, we explore AMIE (Automated-vehicle Multimodal In-cabin Experience), the in-cabin agent responsible for handling multimodal passenger-vehicle interactions. In this work, we discuss the benefits of multimodal understanding of in-cabin utterances by incorporating verbal/language input together with the non-verbal/acoustic and visual input from inside and outside the vehicle. Our experimental results outperformed text-only baselines as we achieved improved performances for intent detection with multimodal approach.

Understanding Affect from video segments has brought researchers from the language, audio and video domains together. Most of the current multimodal research in this area deals with various techniques to fuse the modalities, and mostly treat the segments of a video independently. Motivated by the work of (Zadeh et al., 2017) and (Poria et al., 2017), we present our architecture, Relational Tensor Network, where we use the inter-modal interactions within a segment (intra-segment) and also consider the sequence of segments in a video to model the inter-segment inter-modal interactions. We also generate rich representations of text and audio modalities by leveraging richer audio and linguistic context alongwith fusing fine-grained knowledge based polarity scores from text. We present the results of our model on CMU-MOSEI dataset and show that our model outperforms many baselines and state of the art methods for sentiment classification and emotion recognition.