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Found 26 papers, 5 papers with code
Paraphrase and Solve: Exploring and Exploiting the Impact of Surface Form on Mathematical Reasoning in Large Language Models
This paper studies the relationship between the surface form of a mathematical problem and its solvability by large language models.
Extracting Text Representations for Terms and Phrases in Technical Domains
Extracting dense representations for terms and phrases is a task of great importance for knowledge discovery platforms targeting highly-technical fields.
Unsupervised Term Extraction for Highly Technical Domains
Developing term extractors that are able to generalize across very diverse and potentially highly technical domains is challenging, as annotations for domains requiring in-depth expertise are scarce and expensive to obtain.
Active Learning for Imbalanced Civil Infrastructure Data
Unfortunately, annotation costs are incredibly high as our proprietary civil engineering dataset must be annotated by highly trained engineers.
Textual Explanations and Critiques in Recommendation Systems
This dissertation focuses on two fundamental challenges of addressing this need.
Interlock-Free Multi-Aspect Rationalization for Text Classification
Explanation is important for text classification tasks.
Assistive Recipe Editing through Critiquing
Prior studies have used pre-trained language models, or relied on small paired recipe data (e. g., a recipe paired with a similar one that satisfies a dietary constraint).
Positive and Negative Critiquing for VAE-based Recommenders
As a result of revisiting critiquing from the perspective of multimodal generative models, recent work has proposed M&Ms-VAE, which achieves state-of-the-art performance in terms of recommendation, explanation, and critiquing.
pNLP-Mixer: an Efficient all-MLP Architecture for Language
Large pre-trained language models based on transformer architecture have drastically changed the natural language processing (NLP) landscape.
Multi-Step Critiquing User Interface for Recommender Systems
Recommendations with personalized explanations have been shown to increase user trust and perceived quality and help users make better decisions.
Rationalization through Concepts
One type of explanation is a rationale, i. e., a selection of input features such as relevant text snippets from which the model computes the outcome.
Fast Multi-Step Critiquing for VAE-based Recommender Systems
Experiments on four real-world datasets demonstrate that among state-of-the-art models, our system is the first to dominate or match the performance in terms of recommendation, explanation, and multi-step critiquing.
Recommending Burgers based on Pizza Preferences: Addressing Data Sparsity with a Product of Experts
The intuition is that user-item interactions in a source domain can augment the recommendation quality in a target domain.
An Enhanced MeanSum Method For Generating Hotel Multi-Review Summarizations
Multi-document summaritazion is the process of taking multiple texts as input and producing a short summary text based on the content of input texts.
Modeling Online Behavior in Recommender Systems: The Importance of Temporal Context
Finally, we validate that the Pareto Fronts obtained with the added objective dominate those produced by state-of-the-art models that are only optimized for accuracy on three real-world publicly available datasets.
Momentum-based Gradient Methods in Multi-Objective Recommendation
We evaluate the benefits of Multi-objective Adamize on two multi-objective recommender systems and for three different objective combinations, both correlated or conflicting.
Addressing Fairness in Classification with a Model-Agnostic Multi-Objective Algorithm
The goal of fairness in classification is to learn a classifier that does not discriminate against groups of individuals based on sensitive attributes, such as race and gender.
Interacting with Explanations through Critiquing
Using personalized explanations to support recommendations has been shown to increase trust and perceived quality.
HotelRec: a Novel Very Large-Scale Hotel Recommendation Dataset
In this paper, we propose HotelRec, a very large-scale hotel recommendation dataset, based on TripAdvisor, containing 50 million reviews.
GameWikiSum: a Novel Large Multi-Document Summarization Dataset
In this paper, we propose GameWikiSum, a new domain-specific dataset for multi-document summarization, which is one hundred times larger than commonly used datasets, and in another domain than news.
Multi-Gradient Descent for Multi-Objective Recommender Systems
Recommender systems need to mirror the complexity of the environment they are applied in.
Ranked #1 on
Recommendation Systems
on MovieLens 20M
(Recall@20 metric)
Multi-Dimensional Explanation of Reviews
Neural models achieved considerable improvement for many natural language processing tasks, but they offer little transparency, and interpretability comes at a cost.
Multi-Dimensional Explanation of Target Variables from Documents
Past work used attention and rationale mechanisms to find words that predict the target variable of a document.
Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization
To overcome these limitations, we present a novel method, which makes use of two types of sentence embeddings: universal embeddings, which are trained on a large unrelated corpus, and domain-specific embeddings, which are learned during training.
Dataset Construction via Attention for Aspect Term Extraction with Distant Supervision
Publicly available review corpora contain a plethora of opinionated aspect terms and cover a larger domain spectrum.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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