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Life 3.0: being human in the age of artificial intelligence
- Anne Lauscher
- Art, PsychologyInternet Histories
- 2 January 2019
From Zero to Hero: On the Limitations of Zero-Shot Language Transfer with Multilingual Transformers
It is demonstrated that the inexpensive few-shot transfer (i.e., additional fine-tuning on a few target-language instances) is surprisingly effective across the board, warranting more research efforts reaching beyond the limiting zero-shot conditions.
From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers
- Anne Lauscher, Vinit Ravishankar, Ivan Vulic, Goran Glavas
- Computer Science, LinguisticsArXiv
- 1 May 2020
It is shown that cross-lingual transfer via massively multilingual transformers, much like transfer via cross-lingsual word embeddings, is substantially less effective in resource-lean scenarios and for distant languages.
Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers
- Anne Lauscher, Olga Majewska, Leonardo F. R. Ribeiro, Iryna Gurevych, N. Rozanov, Goran Glavavs
- Computer ScienceDEELIO
- 24 May 2020
A deeper analysis reveals that the adapter-based models substantially outperform BERT on inference tasks that require the type of conceptual knowledge explicitly present in ConceptNet and its corresponding Open Mind Common Sense corpus.
A General Framework for Implicit and Explicit Debiasing of Distributional Word Vector Spaces
Experimental findings across three embedding methods suggest that the proposed debiasing models are robust and widely applicable: they often completely remove the bias both implicitly and explicitly without degradation of semantic information encoded in any of the input distributional spaces.
Sustainable Modular Debiasing of Language Models
An extensive evaluation, encompassing three intrinsic and two extrinsic bias measures, renders A DELE very effective in bias mitigation, and it is shown that – due to its modular nature – ADELE retains fairness even after large-scale downstream training.
Investigating Convolutional Networks and Domain-Specific Embeddings for Semantic Classification of Citations
- Anne Lauscher, Goran Glavas, Simone Paolo Ponzetto, K. Eckert
- Computer ScienceWOSP@JCDL
- 15 December 2017
This work frames polarity and purpose detection as classification tasks and investigates the performance of convolutional networks with general and domain-specific word embeddings on these tasks.
Are We Consistently Biased? Multidimensional Analysis of Biases in Distributional Word Vectors
This work presents a systematic study of biases encoded in distributional word vector spaces, and analyzes how consistent the bias effects are across languages, corpora, and embedding models.
RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models
RedDITBIAS is presented, the first conversational data set grounded in the actual human conversations from Reddit, allowing for bias measurement and mitigation across four important bias dimensions: gender, race, religion, and queerness, and an evaluation framework is developed.
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity
- Anne Lauscher, Ivan Vulic, E. Ponti, A. Korhonen, Goran Glavavs
- Computer ScienceCOLING
- 5 September 2019
The experiments suggest that the standard BERT (LIBERT), specialized for the word-level semantic similarity, yields better performance than the lexically blind “vanilla” BERT on several language understanding tasks, and shows consistent gains on 3 benchmarks for lexical simplification.