Paul Bennett

Also published as: Paul N. Bennett


SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization
Philippe Laban | Tobias Schnabel | Paul N. Bennett | Marti A. Hearst
Transactions of the Association for Computational Linguistics, Volume 10

In the summarization domain, a key requirement for summaries is to be factually consistent with the input document. Previous work has found that natural language inference (NLI) models do not perform competitively when applied to inconsistency detection. In this work, we revisit the use of NLI for inconsistency detection, finding that past work suffered from a mismatch in input granularity between NLI datasets (sentence-level), and inconsistency detection (document level). We provide a highly effective and light-weight method called SummaCConv that enables NLI models to be successfully used for this task by segmenting documents into sentence units and aggregating scores between pairs of sentences. We furthermore introduce a new benchmark called SummaC (Summary Consistency) which consists of six large inconsistency detection datasets. On this dataset, SummaCConv obtains state-of-the-art results with a balanced accuracy of 74.4%, a 5% improvement compared with prior work.

Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations
Ji Xin | Chenyan Xiong | Ashwin Srinivasan | Ankita Sharma | Damien Jose | Paul Bennett
Findings of the Association for Computational Linguistics: ACL 2022

Dense retrieval (DR) methods conduct text retrieval by first encoding texts in the embedding space and then matching them by nearest neighbor search. This requires strong locality properties from the representation space, e.g., close allocations of each small group of relevant texts, which are hard to generalize to domains without sufficient training data. In this paper, we aim to improve the generalization ability of DR models from source training domains with rich supervision signals to target domains without any relevance label, in the zero-shot setting. To achieve that, we propose Momentum adversarial Domain Invariant Representation learning (MoDIR), which introduces a momentum method to train a domain classifier that distinguishes source versus target domains, and then adversarially updates the DR encoder to learn domain invariant representations. Our experiments show that MoDIR robustly outperforms its baselines on 10+ ranking datasets collected in the BEIR benchmark in the zero-shot setup, with more than 10% relative gains on datasets with enough sensitivity for DR models’ evaluation. Source code is available at


Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision
Si Sun | Yingzhuo Qian | Zhenghao Liu | Chenyan Xiong | Kaitao Zhang | Jie Bao | Zhiyuan Liu | Paul Bennett
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

The effectiveness of Neural Information Retrieval (Neu-IR) often depends on a large scale of in-domain relevance training signals, which are not always available in real-world ranking scenarios. To democratize the benefits of Neu-IR, this paper presents MetaAdaptRank, a domain adaptive learning method that generalizes Neu-IR models from label-rich source domains to few-shot target domains. Drawing on source-domain massive relevance supervision, MetaAdaptRank contrastively synthesizes a large number of weak supervision signals for target domains and meta-learns to reweight these synthetic “weak” data based on their benefits to the target-domain ranking accuracy of Neu-IR models. Experiments on three TREC benchmarks in the web, news, and biomedical domains show that MetaAdaptRank significantly improves the few-shot ranking accuracy of Neu-IR models. Further analyses indicate that MetaAdaptRank thrives from both its contrastive weak data synthesis and meta-reweighted data selection. The code and data of this paper can be obtained from

Keep It Simple: Unsupervised Simplification of Multi-Paragraph Text
Philippe Laban | Tobias Schnabel | Paul Bennett | Marti A. Hearst
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

This work presents Keep it Simple (KiS), a new approach to unsupervised text simplification which learns to balance a reward across three properties: fluency, salience and simplicity. We train the model with a novel algorithm to optimize the reward (k-SCST), in which the model proposes several candidate simplifications, computes each candidate’s reward, and encourages candidates that outperform the mean reward. Finally, we propose a realistic text comprehension task as an evaluation method for text simplification. When tested on the English news domain, the KiS model outperforms strong supervised baselines by more than 4 SARI points, and can help people complete a comprehension task an average of 18% faster while retaining accuracy, when compared to the original text.

Say ‘YES’ to Positivity: Detecting Toxic Language in Workplace Communications
Meghana Moorthy Bhat | Saghar Hosseini | Ahmed Hassan Awadallah | Paul Bennett | Weisheng Li
Findings of the Association for Computational Linguistics: EMNLP 2021

Workplace communication (e.g. email, chat, etc.) is a central part of enterprise productivity. Healthy conversations are crucial for creating an inclusive environment and maintaining harmony in an organization. Toxic communications at the workplace can negatively impact overall job satisfaction and are often subtle, hidden, or demonstrate human biases. The linguistic subtlety of mild yet hurtful conversations has made it difficult for researchers to quantify and extract toxic conversations automatically. While offensive language or hate speech has been extensively studied in social communities, there has been little work studying toxic communication in emails. Specifically, the lack of corpus, sparsity of toxicity in enterprise emails, and well-defined criteria for annotating toxic conversations have prevented researchers from addressing the problem at scale. We take the first step towards studying toxicity in workplace emails by providing (1) a general and computationally viable taxonomy to study toxic language at the workplace (2) a dataset to study toxic language at the workplace based on the taxonomy and (3) analysis on why offensive language and hate-speech datasets are not suitable to detect workplace toxicity.

Less is More: Pretrain a Strong Siamese Encoder for Dense Text Retrieval Using a Weak Decoder
Shuqi Lu | Di He | Chenyan Xiong | Guolin Ke | Waleed Malik | Zhicheng Dou | Paul Bennett | Tie-Yan Liu | Arnold Overwijk
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Dense retrieval requires high-quality text sequence embeddings to support effective search in the representation space. Autoencoder-based language models are appealing in dense retrieval as they train the encoder to output high-quality embedding that can reconstruct the input texts. However, in this paper, we provide theoretical analyses and show empirically that an autoencoder language model with a low reconstruction loss may not provide good sequence representations because the decoder may take shortcuts by exploiting language patterns. To address this, we propose a new self-learning method that pre-trains the autoencoder using a weak decoder, with restricted capacity and attention flexibility to push the encoder to provide better text representations. Our experiments on web search, news recommendation, and open domain question answering show that our pre-trained model significantly boosts the effectiveness and few-shot ability of dense retrieval models. Our code is available at


Leveraging Structured Metadata for Improving Question Answering on the Web
Xinya Du | Ahmed Hassan Awadallah | Adam Fourney | Robert Sim | Paul Bennett | Claire Cardie
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

We show that leveraging metadata information from web pages can improve the performance of models for answer passage selection/reranking. We propose a neural passage selection model that leverages metadata information with a fine-grained encoding strategy, which learns the representation for metadata predicates in a hierarchical way. The models are evaluated on the MS MARCO (Nguyen et al., 2016) and Recipe-MARCO datasets. Results show that our models significantly outperform baseline models, which do not incorporate metadata. We also show that the fine-grained encoding’s advantage over other strategies for encoding the metadata.


GATEtoGerManC: A GATE-based Annotation Pipeline for Historical German
Silke Scheible | Richard J. Whitt | Martin Durrell | Paul Bennett
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

We describe a new GATE-based linguistic annotation pipeline for Early Modern German, which can be used to annotate historical texts with word tokens, sentence boundaries, lemmas, and POS tags. The pipeline is based on a customisation of the freely available ANNIE system for English (Cunningham et al., 2002), in combination with a version of the TreeTagger (Schmid, 1994) trained on gold standard Early Modern German data. The POS-tagging and lemmatisation components of the pipeline achieve an average accuracy of 89.44% and 83.16%, respectively, on unseen historical data from various genres and publication dates within the Early Modern period. We show that normalisation of spelling variation can further improve these results. With no specialised tools available for processing this particular stage of the language, this pipeline will be of particular interest to smaller, humanities-based projects wishing to add linguistic annotations to their historical data but which lack the means or resources to develop such tools themselves.


A Gold Standard Corpus of Early Modern German
Silke Scheible | Richard J. Whitt | Martin Durrell | Paul Bennett
Proceedings of the 5th Linguistic Annotation Workshop

Evaluating an ‘off-the-shelf’ POS-tagger on Early Modern German text
Silke Scheible | Richard J. Whitt | Martin Durrell | Paul Bennett
Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities


Combining Probability-Based Rankers for Action-Item Detection
Paul N. Bennett | Jaime G. Carbonell
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference


Reducing boundary friction using translation-fragment overlap
Ralf D. Brown | Rebecca Hutchinson | Paul N. Bennett | Jaime G. Carbonell | Peter Jansen
Proceedings of Machine Translation Summit IX: Papers

Many corpus-based Machine Translation (MT) systems generate a number of partial translations which are then pieced together rather than immediately producing one overall translation. While this makes them more robust to ill-formed input, they are subject to disfluencies at phrasal translation boundaries even for well-formed input. We address this “boundary friction” problem by introducing a method that exploits overlapping phrasal translations and the increased confidence in translation accuracy they imply. We specify an efficient algorithm for producing translations using overlap. Finally, our empirical analysis indicates that this approach produces higher quality translations than the standard method of combining non-overlapping fragments generated by our Example-Based MT (EBMT) system in a peak-to-peak comparison.


Teaching contrastive linguistics for MT
Paul Bennett
Proceedings of the 6th EAMT Workshop: Teaching Machine Translation