516 research outputs found
Whodunit? Learning to Contrast for Authorship Attribution
Authorship attribution is the task of identifying the author of a given text.
The key is finding representations that can differentiate between authors.
Existing approaches typically use manually designed features that capture a
dataset's content and style, but these approaches are dataset-dependent and
yield inconsistent performance across corpora. In this work, we propose
\textit{learning} author-specific representations by fine-tuning pre-trained
generic language representations with a contrastive objective (Contra-X). We
show that Contra-X learns representations that form highly separable clusters
for different authors. It advances the state-of-the-art on multiple human and
machine authorship attribution benchmarks, enabling improvements of up to 6.8%
over cross-entropy fine-tuning. However, we find that Contra-X improves overall
accuracy at the cost of sacrificing performance for some authors. Resolving
this tension will be an important direction for future work. To the best of our
knowledge, we are the first to integrate contrastive learning with pre-trained
language model fine-tuning for authorship attribution.Comment: camera-ready version, AACL-IJCNLP 202
A Survey of Embodied AI: From Simulators to Research Tasks
There has been an emerging paradigm shift from the era of "internet AI" to
"embodied AI", where AI algorithms and agents no longer learn from datasets of
images, videos or text curated primarily from the internet. Instead, they learn
through interactions with their environments from an egocentric perception
similar to humans. Consequently, there has been substantial growth in the
demand for embodied AI simulators to support various embodied AI research
tasks. This growing interest in embodied AI is beneficial to the greater
pursuit of Artificial General Intelligence (AGI), but there has not been a
contemporary and comprehensive survey of this field. This paper aims to provide
an encyclopedic survey for the field of embodied AI, from its simulators to its
research. By evaluating nine current embodied AI simulators with our proposed
seven features, this paper aims to understand the simulators in their provision
for use in embodied AI research and their limitations. Lastly, this paper
surveys the three main research tasks in embodied AI -- visual exploration,
visual navigation and embodied question answering (QA), covering the
state-of-the-art approaches, evaluation metrics and datasets. Finally, with the
new insights revealed through surveying the field, the paper will provide
suggestions for simulator-for-task selections and recommendations for the
future directions of the field.Comment: Under Review for IEEE TETC
Robustness of Utilizing Feedback in Embodied Visual Navigation
This paper presents a framework for training an agent to actively request
help in object-goal navigation tasks, with feedback indicating the location of
the target object in its field of view. To make the agent more robust in
scenarios where a teacher may not always be available, the proposed training
curriculum includes a mix of episodes with and without feedback. The results
show that this approach improves the agent's performance, even in the absence
of feedback.Comment: Accepted at the ICRA Workshop for Communicating Robot Learning across
Human-Robot Interactio
It's Morphin' Time! Combating Linguistic Discrimination with Inflectional Perturbations
Training on only perfect Standard English corpora predisposes pre-trained
neural networks to discriminate against minorities from non-standard linguistic
backgrounds (e.g., African American Vernacular English, Colloquial Singapore
English, etc.). We perturb the inflectional morphology of words to craft
plausible and semantically similar adversarial examples that expose these
biases in popular NLP models, e.g., BERT and Transformer, and show that
adversarially fine-tuning them for a single epoch significantly improves
robustness without sacrificing performance on clean data.Comment: To appear in the Proceedings of the 58th Annual Meeting of the
Association for Computational Linguistics (ACL 2020
Using closely-related language to build an ASR for a very under-resourced language: Iban
International audienceThis paper describes our work on automatic speech recognition system (ASR) for an under-resourced language, Iban, a language that is mainly spoken in Sarawak, Malaysia. We collected 8 hours of data to begin this study due to no resources for ASR exist. We employed bootstrapping techniques involving a closely-related language for rapidly building and improve an Iban system. First, we used already available data from Malay, a local dominant language in Malaysia, to bootstrap grapheme-to-phoneme system (G2P) for the target language. We also built various types of G2Ps, including a grapheme-based and an English G2P, to produce different versions of dictionaries. We tested all of the dictionaries on the Iban ASR to provide us the best version. Second, we improved the baseline GMM system word error rate (WER) result by utilizing subspace Gaussian mixture models (SGMM). To test, we set two levels of data sparseness on Iban data; 7 hours and 1 hour transcribed speech. We investigated cross-lingual SGMM where the shared parameters were obtained either in monolingual or multilingual fashion and then applied to the target language for training. Experiments on out-of-language data, English and Malay, as source languages result in lower WERs when Iban data is very limited
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