Author pages are created from data sourced from our academic publisher partnerships and public sources.
Share This Author
MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance
- Wei Zhao, Maxime Peyrard, Fei Liu, Yang Gao, Christian M. Meyer, Steffen Eger
- Computer ScienceEMNLP
- 14 August 2019
This paper investigates strategies to encode system and reference texts to devise a metric that shows a high correlation with human judgment of text quality and validate the new metric, namely MoverScore, on a number of text generation tasks.
Neural End-to-End Learning for Computational Argumentation Mining
This work investigates neural techniques for end-to-end computational argumentation mining and finds that jointly learning 'natural' subtasks, in a multi-task learning setup, improves performance.
Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems
This work investigates the impact of visual adversarial attacks on current NLP systems on character-, word-, and sentence-level tasks, showing that both neural and non-neural models are, in contrast to humans, extremely sensitive to such attacks, suffering performance decreases of up to 82%.
What is the Essence of a Claim? Cross-Domain Claim Identification
- Johannes Daxenberger, Steffen Eger, Ivan Habernal, Christian Stab, Iryna Gurevych
- Computer ScienceEMNLP
- 24 April 2017
While the divergent conceptualization of claims in different datasets is indeed harmful to cross-domain classification, it is shown that there are shared properties on the lexical level as well as system configurations that can help to overcome these gaps.
Concatenated p-mean Word Embeddings as Universal Cross-Lingual Sentence Representations
It is shown that the concatenation of different types of power mean word embeddings considerably closes the gap to state-of-the-art methods monolingually and substantially outperforms these more complex techniques cross-lingually.
SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization
This work proposes SUPERT, which rates the quality of a summary by measuring its semantic similarity with a pseudo reference summary, i.e. selected salient sentences from the source documents, using contextualized embeddings and soft token alignment techniques.
ArgumenText: Searching for Arguments in Heterogeneous Sources
This paper presents an argument retrieval system capable of retrieving sentential arguments for any given controversial topic, and finds that its system covers 89% of arguments found in expert-curated lists of arguments from an online debate portal, and also identifies additional valid arguments.
On the Linearity of Semantic Change: Investigating Meaning Variation via Dynamic Graph Models
It is found that semantic change is linear in two senses: today’s embedding vector (= meaning) of words can be derived as linear combinations of embedding vectors of their neighbors in previous time periods.
Lexicon-assisted tagging and lemmatization in Latin: A comparison of six taggers and two lemmatization methods
We present a survey of tagging accuracies — concerning part-of-speech and full morphological tagging — for several taggers based on a corpus for medieval church Latin (see www.comphistsem.org). The…
Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications
An agreement score to evaluate the performance of routing processes at instance-level, an adaptive optimizer to enhance the reliability of routing, and capsule compression and partial routing to improve the scalability of capsule networks are introduced.