More Like this

1. Large Language Model Augmented Narrative Driven Recommendations

   https://doi.org/10.1145/3604915  

   Mysore, Sheshera; McCallum, Andrew; Zamani, Hamed (September 2023, ACM)

   Zhang, Jie; Chen, Li; Berkovsky, Shlomo; Zhang, Min; Noia, Tommaso di; Basilico, Justin; Pizzato, Luiz; Song, Yang (Ed.)

   Narrative-driven recommendation (NDR) presents an information access problem where users solicit recommendations with verbose descriptions of their preferences and context, for example, travelers soliciting recommendations for points of interest while describing their likes/dislikes and travel circumstances. These requests are increasingly important with the rise of natural language-based conversational interfaces for search and recommendation systems. However, NDR lacks abundant training data for models, and current platforms commonly do not support these requests. Fortunately, classical user-item interaction datasets contain rich textual data, e.g., reviews, which often describe user preferences and context – this may be used to bootstrap training for NDR models. In this work, we explore using large language models (LLMs) for data augmentation to train NDR models. We use LLMs for authoring synthetic narrative queries from user-item interactions with few-shot prompting and train retrieval models for NDR on synthetic queries and user-item interaction data. Our experiments demonstrate that this is an effective strategy for training small-parameter retrieval models that outperform other retrieval and LLM baselines for narrative-driven recommendation. 

   more » « less
2. Large Language Model Augmented Narrative Driven Recommendations

   https://doi.org/10.1145/3604915.3608829  

   Mysore, Sheshera; Mccallum, Andrew; Zamani, Hamed (September 2023, ACM)

   Narrative-driven recommendation (NDR) presents an information access problem where users solicit recommendations with verbose descriptions of their preferences and context, for example, travelers soliciting recommendations for points of interest while describing their likes/dislikes and travel circumstances. These requests are increasingly important with the rise of natural language-based conversational interfaces for search and recommendation systems. However, NDR lacks abundant training data for models, and current platforms commonly do not support these requests. Fortunately, classical user-item interaction datasets contain rich textual data, e.g., reviews, which often describe user preferences and context – this may be used to bootstrap training for NDR models. In this work, we explore using large language models (LLMs) for data augmentation to train NDR models. We use LLMs for authoring synthetic narrative queries from user-item interactions with few-shot prompting and train retrieval models for NDR on synthetic queries and user-item interaction data. Our experiments demonstrate that this is an effective strategy for training small-parameter retrieval models that outperform other retrieval and LLM baselines for narrative-driven recommendation. 

   more » « less
3. Rethinking Conversational Recommendations: Is Decision Tree All You Need?

   https://doi.org/10.1145/3511808.3557433  

   Haque, A S; Wang, Hongning (October 2022, Proceedings of the 31st ACM International Conference on Information & Knowledge Management)

   Conversational recommender systems (CRS) dynamically obtain the users' preferences via multi-turn questions and answers. The existing CRS solutions are widely dominated by deep reinforcement learning algorithms. However, deep reinforcement learning methods are often criticized for lacking interpretability and requiring a large amount of training data to perform.In this paper, we explore a simpler alternative and propose a decision tree based solution to CRS. The underlying challenge in CRS is that the same item can be described differently by different users. We show that decision trees are sufficient to characterize the interactions between users and items, and solve the key challenges in multi-turn CRS: namely which questions to ask, how to rank the candidate items, when to recommend, and how to handle user's negative feedback on the recommendations. Firstly, the training of decision trees enables us to find questions which effectively narrow down the search space. Secondly, by learning embeddings for each item and tree nodes, the candidate items can be ranked based on their similarity to the conversation context encoded by the tree nodes. Thirdly, the diversity of items associated with each tree node allows us to develop an early stopping strategy to decide when to make recommendations. Fourthly, when the user rejects a recommendation, we adaptively choose the next decision tree to improve subsequent questions and recommendations. Extensive experiments on three publicly available benchmark CRS datasets show that our approach provides significant improvement to the state of the art CRS methods. 

   more » « less
4. Assessing Child Communication Engagement via Speech Recognition in Naturalistic Active Learning Spaces

   https://doi.org/10.21437/Odyssey.2020-56  

   Lileikyte, Rasa; Irvin, Dwight; Hansen, John H. (November 2020, ISCA ODYSSEY-2020)

   The ability to assess children’s conversational interaction is critical in determining language and cognitive proficiency for typically developing and at-risk children. The earlier at-risk child is identified, the earlier support can be provided to reduce the social impact of the speech disorder. To date, limited research has been performed for young child speech recognition in classroom settings. This study addresses speech recognition research with naturalistic children’s speech, where age varies from 2.5 to 5 years. Data augmentation is relatively under explored for child speech. Therefore, we investigate the effectiveness of data augmentation techniques to improve both language and acoustic models. We explore alternate text augmentation approaches using adult data, Web data, and via text generated by recurrent neural networks. We also compare several acoustic augmentation techniques: speed perturbation, tempo perturbation, and adult data. Finally, we comment on child word count rates to assess child speech development. 

   more » « less
5. Towards Domain-Agnostic and Domain-Adaptive Dementia Detection from Spoken Language

   https://doi.org/10.18653/v1/2023.acl-long.668  

   Farzana, Shahla; Parde, Natalie (January 2023, Association for Computational Linguistics)

   Health-related speech datasets are often small and varied in focus. This makes it difficult to leverage them to effectively support healthcare goals. Robust transfer of linguistic features across different datasets orbiting the same goal carries potential to address this concern. To test this hypothesis, we experiment with domain adaptation (DA) techniques on heterogeneous spoken language data to evaluate generalizability across diverse datasets for a common task: dementia detection. We find that adapted models exhibit better performance across conversational and task-oriented datasets. The feature-augmented DA method achieves a 22% increase in accuracy adapting from a conversational to task-specific dataset compared to a jointly trained baseline. This suggests promising capacity of these techniques to allow for productive use of disparate data for a complex spoken language healthcare task. 

   more » « less