Six papers from CS researchers were accepted to the 16th conference of the European Chapter of the Association for Computational Linguistics (EACL). As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.
Below are brief descriptions and links to the papers.
Event-Driven News Stream Clustering using Entity-Aware Contextual Embeddings
Kailash Karthik Saravanakumar Columbia University, Miguel Ballesteros Amazon AI, Muthu Kumar Chandrasekaran Amazon AI, Kathleen McKeown Columbia University & Amazon AI
This paper presents a new clustering paradigm for news streams, where clusters have a one-to-one correspondence with real-world events (for example, the Suez canal blockage). An important aspect of this problem is that the number of clusters is unknown and varies with time (new events occur and old events cease to be of relevance). The proposed paradigm follows a pipeline approach – where representations are built for each new article, comparisons are made with existing clusters to pick the most compatible one, and finally, a clustering decision is produced.
A surprising observation from this work is that contextual embeddings (from models like BERT), in contrast to their overwhelming success in many NLP problems, achieve sub-par performance by themselves on this clustering problem. However, when combined with other representations (like TF-IDF and timestamps) and fine-tuned with task-specific augmentations, they achieve new state-of-the-art performance. Another interesting observation is that the widely reported B-Cubed metrics are biased towards large clusters and hence don’t capture cluster fragmentation on smaller clusters as well. Since clusters corresponding to emerging events are small and errors made on such clusters are highly undesirable, the authors suggest using an additional metric CEAF-e to evaluate models for this task.
Segmenting Subtitles for Correcting ASR Segmentation Errors
David Wan Columbia University, Chris Kedzie Columbia University, Faisal Ladakh Columbia University, Elsbeth Turcan Columbia University, Petra Galuszkova University of Maryland, Elena Zotkina University of Maryland, Zhengping Jiang Columbia University, Peter Bell University of Edinburgh, and Kathleen McKeown Columbia University
For the task of spoken language translation, the usual approach is to have a pipeline consisting of Automatic Speech Recognition (ASR) that transforms audio files into words and utterances in the original language and a Machine Translation (MT) that translate the utterances into the target language. However this setup may suffer from input-output mismatches: ASR segments utterances by acoustic information such as pauses, and thus may produce run-on sentences or sentence fragments, but MT is usually trained on proper sentences without such issues and may not perform well under such setting. This paper proposes the use of an intermediate model to segment utterances into sentences to improve performance in MT as well as other downstream tasks.
One crucial problem for developing such models is the lack of suitable training data for segmentation, especially when the languages involved are low-resourced. To this end, this paper also proposes a way to use subtitles dataset as proxy speech data as well as creating synthetic acoustic utterances that mimic common ASR errors for the model to learn to fix. Using a simple neural tagging model, the authors of this paper show improvement over the baseline ASR segmentation on MT for Lithuanian, Bulgarian, and Farisi. A surprising finding is that the segmentation model most improves the translation quality of more syntactically complex segments.
“Talk to me with left, right, and angles”: Lexical entrainment in spoken Hebrew dialogue
Andreas Weise CUNY Graduate Center, Vered Silber-Varod The Open University of Israel, Anat Lerner The Open University of Israel, Julia Hirschberg Columbia University, and Rivka Levitan Columbia University
It has been well-documented for several languages that human interlocutors tend to adapt their linguistic productions to become more similar to each other. This behavior, known as entrainment, affects lexical choice as well, both with regard to specific words, such as referring expressions, and overall style.
Lexical entrainment is the behavior that causes the words that speakers use in a conversation to become more similar over time. Entrainment more broadly is a human behavior causing interlocutors to adapt to each other to become more similar. Its effects are measurable but entrainment itself is not a measure.
This paper offers the first investigation of such lexical entrainment in Hebrew.
The analysis of Hebrew speakers interacting in a Map Task, a popular experimental setup, provides rich evidence of lexical entrainment. No clear pattern of differences is found between speaker pairs by the combination of their genders, nor between speakers by their individual gender. However, speakers in a position of less power are found to entrain more than those with greater power, which matches theoretical accounts.
Overall, the results mostly accord with those for American English. There is, however, a surprising lack of entrainment on a list of hedge words that were previously found to be highly entrained in English. This might be due to cultural differences between American and Israeli speakers that render adoption of a more tentative style less appropriate in the Hebrew context.
Entity-level Factual Consistency of Abstractive Text Summarization
Feng Nan Amazon Web Services, Ramesh Nallapati Amazon Web Services, Zhiguo Wang Amazon Web Services, Cicero Nogueira dos Santos Amazon Web Services, Henghui Zhu Amazon Web Services, Dejiao Zhang Amazon Web Services, Kathleen McKeown Amazon Web Services & Columbia University, Bing Xiang Amazon Web Services
A key challenge for abstractive summarization is ensuring factual consistency of the generated summary with respect to the original document. For example, state-of-the-art models trained on existing datasets exhibit entity hallucination, generating names of entities that are not present in the source document.
The paper proposes a set of new metrics to quantify the entity-level factual consistency of generated summaries and shows that the entity hallucination problem can be alleviated by simply filtering the training data. In addition, the paper introduces a summary-worthy entity classification task to the training process as well as a joint entity and summary generation approach, which yields further improvements in entity-level metrics.
“Laughing at you or with you”: The Role of Sarcasm in Shaping the Disagreement Space
Debanjan Ghosh Educational Testing Service, Ritvik Shrivastava MindMeld, Cisco Systems & Columbia University, and Smaranda Muresan Columbia University
Detecting arguments in online interactions is useful to understand how conflicts arise and get resolved. Users often use figurative language, such as sarcasm, either as persuasive devices or to attack the opponent by an ad hominem argument. To further our understanding of the role of sarcasm in shaping the disagreement space, the paper presents a thorough experimental setup using a corpus annotated with both argumentative moves (agree/disagree) and sarcasm. The research exploits joint modeling in terms of (a) applying discrete features that are useful in detecting sarcasm to the task of argumentative relation classification (agree/disagree/none), and (b) multitask learning for argumentative relation classification and sarcasm detection using deep learning architectures (e.g., dual Long ShortTerm Memory (LSTM) with hierarchical attention and Transformer-based architectures). The paper shows that modeling sarcasm improves the argumentative relation classification task (agree/disagree/none) in all setups.
A Unified Feature Representation for Lexical Connotations
Emily Allaway Columbia University and Kathleen McKeown Columbia University
Ideological attitudes and stances are often expressed through subtle meanings of words and phrases. Understanding these connotations is critical to recognize the cultural and emotional perspectives of the speaker. In this paper, the researchers use distant labeling to create a new lexical resource representing connotation aspects for nouns and adjectives. Their analysis shows that it aligns well with human judgments. Additionally, they present a method for creating lexical representations that capture connotations within the embedding space and show that using the embeddings provides a statistically significant improvement on the task of stance detection when data is limited.