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Found 54 papers, 36 papers with code
Reasoning over RDF Knowledge Bases using Deep Learning
Semantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field.
Experimental neural network enhanced quantum tomography
We compared the neural network state reconstruction protocol with a protocol treating SPAM errors by process tomography, as well as to a SPAM-agnostic protocol with idealized measurements.
Training Temporal Word Embeddings with a Compass
Temporal word embeddings have been proposed to support the analysis of word meaning shifts during time and to study the evolution of languages.
What the [MASK]? Making Sense of Language-Specific BERT Models
Driven by the potential of BERT models, the NLP community has started to investigate and generate an abundant number of BERT models that are trained on a particular language, and tested on a specific data domain and task.
"An Image is Worth a Thousand Features": Scalable Product Representations for In-Session Type-Ahead Personalization
We address the problem of personalizing query completion in a digital commerce setting, in which the bounce rate is typically high and recurring users are rare.
Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence
Topic models extract groups of words from documents, whose interpretation as a topic hopefully allows for a better understanding of the data.
Compass-aligned Distributional Embeddings for Studying Semantic Differences across Corpora
In this paper, we present a general framework to support cross-corpora language studies with word embeddings, where embeddings generated from different corpora can be compared to find correspondences and differences in meaning across the corpora.
Cross-lingual Contextualized Topic Models with Zero-shot Learning
They all cover the same content, but the linguistic differences make it impossible to use traditional, bag-of-word-based topic models.
Knowledge Graph Embeddings and Explainable AI
Knowledge graph embeddings are now a widely adopted approach to knowledge representation in which entities and relationships are embedded in vector spaces.
``You Sound Just Like Your Father'' Commercial Machine Translation Systems Include Stylistic Biases
The main goal of machine translation has been to convey the correct content.
Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario
This paper addresses the challenge of leveraging multiple embedding spaces for multi-shop personalization, proving that zero-shot inference is possible by transferring shopping intent from one website to another without manual intervention.
Tough Tables: Carefully Evaluating Entity Linking for Tabular Data
Table annotation is a key task to improve querying the Web and support the Knowledge Graph population from legacy sources (tables).
BERT Goes Shopping: Comparing Distributional Models for Product Representations
Word embeddings (e. g., word2vec) have been applied successfully to eCommerce products through~\textit{prod2vec}.
BERTective: Language Models and Contextual Information for Deception Detection
In most cases, however, the target texts{'} preceding context is not considered.
Query2Prod2Vec Grounded Word Embeddings for eCommerce
We present Query2Prod2Vec, a model that grounds lexical representations for product search in product embeddings: in our model, meaning is a mapping between words and a latent space of products in a digital shop.
Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction
We investigate grounded language learning through real-world data, by modelling a teacher-learner dynamics through the natural interactions occurring between users and search engines; in particular, we explore the emergence of semantic generalization from unsupervised dense representations outside of synthetic environments.
SIGIR 2021 E-Commerce Workshop Data Challenge
The 2021 SIGIR workshop on eCommerce is hosting the Coveo Data Challenge for "In-session prediction for purchase intent and recommendations".
Words with Consistent Diachronic Usage Patterns are Learned Earlier: A Computational Analysis Using Temporally Aligned Word Embeddings
In this study, we use temporally aligned word embeddings and a large diachronic corpus of English to quantify language change in a data-driven, scalable way, which is grounded in language use.
HONEST: Measuring Hurtful Sentence Completion in Language Models
Our results show that 4. 3{\%} of the time, language models complete a sentence with a hurtful word.
Ranked #1 on
Hurtful Sentence Completion
on HONEST
Query2Prod2Vec: Grounded Word Embeddings for eCommerce
We present Query2Prod2Vec, a model that grounds lexical representations for product search in product embeddings: in our model, meaning is a mapping between words and a latent space of products in a digital shop.
Contrastive Language-Image Pre-training for the Italian Language
CLIP (Contrastive Language-Image Pre-training) is a very recent multi-modal model that jointly learns representations of images and texts.
SWEAT: Scoring Polarization of Topics across Different Corpora
Understanding differences of viewpoints across corpora is a fundamental task for computational social sciences.
Language Invariant Properties in Natural Language Processing
We introduce language invariant properties: i. e., properties that should not change when we transform text, and how they can be used to quantitatively evaluate the robustness of transformation algorithms.
Beyond NDCG: behavioral testing of recommender systems with RecList
As with most Machine Learning systems, recommender systems are typically evaluated through performance metrics computed over held-out data points.
Twitter-Demographer: A Flow-based Tool to Enrich Twitter Data
Twitter data have become essential to Natural Language Processing (NLP) and social science research, driving various scientific discoveries in recent years.
"Does it come in black?" CLIP-like models are zero-shot recommenders
Product discovery is a crucial component for online shopping.
Contrastive language and vision learning of general fashion concepts
The steady rise of online shopping goes hand in hand with the development of increasingly complex ML and NLP models.
EvalRS: a Rounded Evaluation of Recommender Systems
Much of the complexity of Recommender Systems (RSs) comes from the fact that they are used as part of more complex applications and affect user experience through a varied range of user interfaces.
Real-Time Oil Leakage Detection on Aftermarket Motorcycle Damping System with Convolutional Neural Networks
In this work, we describe in detail how Deep Learning and Computer Vision can help to detect fault events of the AirTender system, an aftermarket motorcycle damping system component.
When and why vision-language models behave like bags-of-words, and what to do about it?
ARO consists of Visual Genome Attribution, to test the understanding of objects' properties; Visual Genome Relation, to test for relational understanding; and COCO & Flickr30k-Order, to test for order sensitivity.
Is It Worth the (Environmental) Cost? Limited Evidence for Temporal Adaptation via Continuous Training
Consequently, we should continuously update our models with new data to expose them to new events and facts.
Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages
More data is needed, but annotating hateful content is expensive, time-consuming and potentially harmful to annotators.
ProSiT! Latent Variable Discovery with PROgressive SImilarity Thresholds
The most common ways to explore latent document dimensions are topic models and clustering methods.
"It's Not Just Hate'': A Multi-Dimensional Perspective on Detecting Harmful Speech Online
Well-annotated data is a prerequisite for good Natural Language Processing models.
Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale
For example, we find cases of prompting for basic traits or social roles resulting in images reinforcing whiteness as ideal, prompting for occupations resulting in amplification of racial and gender disparities, and prompting for objects resulting in reification of American norms.
SocioProbe: What, When, and Where Language Models Learn about Sociodemographics
Our results show that PLMs do encode these sociodemographics, and that this knowledge is sometimes spread across the layers of some of the tested PLMs.
Beyond Digital "Echo Chambers": The Role of Viewpoint Diversity in Political Discussion
We find that the diversity scores for both Fragmentation and Representation are lower for immigration than for DST.
Leveraging medical Twitter to build a visual–language foundation model for pathology AI
This is the largest public dataset for pathology images annotated with natural text.
EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deployments
EvalRS aims to bring together practitioners from industry and academia to foster a debate on rounded evaluation of recommender systems, with a focus on real-world impact across a multitude of deployment scenarios.
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender Systems
Recommender Systems today are still mostly evaluated in terms of accuracy, with other aspects beyond the immediate relevance of recommendations, such as diversity, long-term user retention and fairness, often taking a back seat.
XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models
In this paper, we introduce a new test suite called XSTest to identify such eXaggerated Safety behaviours in a systematic way.
Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions
Training large language models to follow instructions makes them perform better on a wide range of tasks and generally become more helpful.
How Well Can LLMs Negotiate? NegotiationArena Platform and Analysis
We develop NegotiationArena: a flexible framework for evaluating and probing the negotiation abilities of LLM agents.
Large Language Models are Vulnerable to Bait-and-Switch Attacks for Generating Harmful Content
The risks derived from large language models (LLMs) generating deceptive and damaging content have been the subject of considerable research, but even safe generations can lead to problematic downstream impacts.
MilaNLP at SemEval-2022 Task 5: Using Perceiver IO for Detecting Misogynous Memes with Text and Image Modalities
In this paper, we describe the system proposed by the MilaNLP team for the Multimedia Automatic Misogyny Identification (MAMI) challenge.
Measuring Harmful Sentence Completion in Language Models for LGBTQIA+ Individuals
Current language technology is ubiquitous and directly influences individuals’ lives worldwide.
XLM-EMO: Multilingual Emotion Prediction in Social Media Text
Detecting emotion in text allows social and computational scientists to study how people behave and react to online events.
Pipelines for Social Bias Testing of Large Language Models
We hope to open a discussion on the best methodologies to handle social bias testing in language models.
MilaNLP @ WASSA: Does BERT Feel Sad When You Cry?
The paper describes the MilaNLP team’s submission (Bocconi University, Milan) in the WASSA 2021 Shared Task on Empathy Detection and Emotion Classification.
HATE-ITA: New Baselines for Hate Speech Detection in Italian
Online hate speech is a dangerous phenomenon that can (and should) be promptly counteracted properly.
FEEL-IT: Emotion and Sentiment Classification for the Italian Language
While sentiment analysis is a popular task to understand people’s reactions online, we often need more nuanced information: is the post negative because the user is angry or sad?
Universal Joy A Data Set and Results for Classifying Emotions Across Languages
While emotions are universal aspects of human psychology, they are expressed differently across different languages and cultures.
“Does it come in black?” CLIP-like models are zero-shot recommenders
Product discovery is a crucial component for online shopping.
