pwc-archive/papers-with-abstracts huggingface dataset:
RetrievalSum: A Retrieval Enhanced Framework for Abstractive Summarization
Authors: Chenxin An, Ming Zhong, Zhichao Geng, Jianqiang Yang, Xipeng Qiu
Date: 2021-09-16 00:00:00
Tasks: Abstractive Text Summarization, Retrieval

Existing summarization systems mostly generate summaries purely relying on the content of the source document. However, even for humans, we usually need some references or exemplars to help us fully understand the source document and write summaries in a particular format. But how to find the high-quality exemplars and incorporate them into summarization systems is still challenging and worth exploring. In this paper, we propose RetrievalSum, a novel retrieval enhanced abstractive summarization framework consisting of a dense Retriever and a Summarizer. At first, several closely related exemplars are retrieved as supplementary input to help the generation model understand the text more comprehensively. Furthermore, retrieved exemplars can also play a role in guiding the model to capture the writing style of a specific corpus. We validate our method on a wide range of summarization datasets across multiple domains and two backbone models: BERT and BART. Results show that our framework obtains significant improvement by 1.38~4.66 in ROUGE-1 score when compared with the powerful pre-trained models, and achieve new state-of-the-art on BillSum. Human evaluation demonstrates that our retrieval enhanced model can better capture the domain-specific writing style.
Efficient Attribute Injection for Pretrained Language Models
Authors: Reinald Kim Amplayo, Kang Min Yoo, Sang-Woo Lee
Date: 2021-09-16 00:00:00
Tasks: Attribute

Metadata attributes (e.g., user and product IDs from reviews) can be incorporated as additional inputs to neural-based NLP models, by modifying the architecture of the models, in order to improve their performance. Recent models however rely on pretrained language models (PLMs), where previously used techniques for attribute injection are either nontrivial or ineffective. In this paper, we propose a lightweight and memory-efficient method to inject attributes to PLMs. We extend adapters, i.e. tiny plug-in feed-forward modules, to include attributes both independently of or jointly with the text. To limit the increase of parameters especially when the attribute vocabulary is large, we use low-rank approximations and hypercomplex multiplications, significantly decreasing the total parameters. We also introduce training mechanisms to handle domains in which attributes can be multi-labeled or sparse. Extensive experiments and analyses on eight datasets from different domains show that our method outperforms previous attribute injection methods and achieves state-of-the-art performance on various datasets.
Improving Unsupervised Question Answering via Summarization-Informed Question Generation
Authors: Chenyang Lyu, Lifeng Shang, Yvette Graham, Jennifer Foster, Xin Jiang, Qun Liu
Date: 2021-09-16 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Articles, Dependency Parsing, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Natural Questions, Question Answering, Question Generation, Question-Generation, Semantic Role Labeling, TriviaQA

Question Generation (QG) is the task of generating a plausible question for a given <passage, answer> pair. Template-based QG uses linguistically-informed heuristics to transform declarative sentences into interrogatives, whereas supervised QG uses existing Question Answering (QA) datasets to train a system to generate a question given a passage and an answer. A disadvantage of the heuristic approach is that the generated questions are heavily tied to their declarative counterparts. A disadvantage of the supervised approach is that they are heavily tied to the domain/language of the QA dataset used as training data. In order to overcome these shortcomings, we propose an unsupervised QG method which uses questions generated heuristically from summaries as a source of training data for a QG system. We make use of freely available news summary data, transforming declarative summary sentences into appropriate questions using heuristics informed by dependency parsing, named entity recognition and semantic role labeling. The resulting questions are then combined with the original news articles to train an end-to-end neural QG model. We extrinsically evaluate our approach using unsupervised QA: our QG model is used to generate synthetic QA pairs for training a QA model. Experimental results show that, trained with only 20k English Wikipedia-based synthetic QA pairs, the QA model substantially outperforms previous unsupervised models on three in-domain datasets (SQuAD1.1, Natural Questions, TriviaQA) and three out-of-domain datasets (NewsQA, BioASQ, DuoRC), demonstrating the transferability of the approach.
TruthfulQA: Measuring How Models Mimic Human Falsehoods
Authors: Stephanie Lin, Jacob Hilton, Owain Evans
Date: 2021-09-08 00:00:00
Proceeding: ACL 2022 5
Tasks: Language Modeling, Language Modelling, Misconceptions, Question Answering, Question Generation, TruthfulQA

We propose a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. We crafted questions that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers learned from imitating human texts. We tested GPT-3, GPT-Neo/J, GPT-2 and a T5-based model. The best model was truthful on 58% of questions, while human performance was 94%. Models generated many false answers that mimic popular misconceptions and have the potential to deceive humans. The largest models were generally the least truthful. This contrasts with other NLP tasks, where performance improves with model size. However, this result is expected if false answers are learned from the training distribution. We suggest that scaling up models alone is less promising for improving truthfulness than fine-tuning using training objectives other than imitation of text from the web.
Alquist 4.0: Towards Social Intelligence Using Generative Models and Dialogue Personalization
Authors: Jakub Konrád, Jan Pichl, Petr Marek, Petr Lorenc, Van Duy Ta, Ondřej Kobza, Lenka Hýlová, Jan Šedivý
Date: 2021-09-16 00:00:00

The open domain-dialogue system Alquist has a goal to conduct a coherent and engaging conversation that can be considered as one of the benchmarks of social intelligence. The fourth version of the system, developed within the Alexa Prize Socialbot Grand Challenge 4, brings two main innovations. The first addresses coherence, and the second addresses the engagingness of the conversation. For innovations regarding coherence, we propose a novel hybrid approach combining hand-designed responses and a generative model. The proposed approach utilizes hand-designed dialogues, out-of-domain detection, and a neural response generator. Hand-designed dialogues walk the user through high-quality conversational flows. The out-of-domain detection recognizes that the user diverges from the predefined flow and prevents the system from producing a scripted response that might not make sense for unexpected user input. Finally, the neural response generator generates a response based on the context of the dialogue that correctly reacts to the unexpected user input and returns the dialogue to the boundaries of hand-designed dialogues. The innovations for engagement that we propose are mostly inspired by the famous exploration-exploitation dilemma. To conduct an engaging conversation with the dialogue partners, one has to learn their preferences and interests -- exploration. Moreover, to engage the partner, we have to utilize the knowledge we have already learned -- exploitation. In this work, we present the principles and inner workings of individual components of the open-domain dialogue system Alquist developed within the Alexa Prize Socialbot Grand Challenge 4 and the experiments we have conducted to evaluate them.
Do Language Models Know the Way to Rome?
Authors: Bastien Liétard, Mostafa Abdou, Anders Søgaard
Date: 2021-09-16 00:00:00
Proceeding: EMNLP (BlackboxNLP) 2021 11
Tasks: Language Modeling, Language Modelling

The global geometry of language models is important for a range of applications, but language model probes tend to evaluate rather local relations, for which ground truths are easily obtained. In this paper we exploit the fact that in geography, ground truths are available beyond local relations. In a series of experiments, we evaluate the extent to which language model representations of city and country names are isomorphic to real-world geography, e.g., if you tell a language model where Paris and Berlin are, does it know the way to Rome? We find that language models generally encode limited geographic information, but with larger models performing the best, suggesting that geographic knowledge can be induced from higher-order co-occurrence statistics.
Let the CAT out of the bag: Contrastive Attributed explanations for Text
Authors: Saneem Chemmengath, Amar Prakash Azad, Ronny Luss, Amit Dhurandhar
Date: 2021-09-16 00:00:00
Tasks: Attribute, Language Modeling, Language Modelling

Contrastive explanations for understanding the behavior of black box models has gained a lot of attention recently as they provide potential for recourse. In this paper, we propose a method Contrastive Attributed explanations for Text (CAT) which provides contrastive explanations for natural language text data with a novel twist as we build and exploit attribute classifiers leading to more semantically meaningful explanations. To ensure that our contrastive generated text has the fewest possible edits with respect to the original text, while also being fluent and close to a human generated contrastive, we resort to a minimal perturbation approach regularized using a BERT language model and attribute classifiers trained on available attributes. We show through qualitative examples and a user study that our method not only conveys more insight because of these attributes, but also leads to better quality (contrastive) text. Quantitatively, we show that our method outperforms other state-of-the-art methods across four data sets on four benchmark metrics.
Context-aware Entity Typing in Knowledge Graphs
Authors: Weiran Pan, Wei Wei, Xian-Ling Mao
Date: 2021-09-16 00:00:00
Proceeding: Findings (EMNLP) 2021 11
Tasks: Entity Typing, Knowledge Graphs

Knowledge graph entity typing aims to infer entities' missing types in knowledge graphs which is an important but under-explored issue. This paper proposes a novel method for this task by utilizing entities' contextual information. Specifically, we design two inference mechanisms: i) N2T: independently use each neighbor of an entity to infer its type; ii) Agg2T: aggregate the neighbors of an entity to infer its type. Those mechanisms will produce multiple inference results, and an exponentially weighted pooling method is used to generate the final inference result. Furthermore, we propose a novel loss function to alleviate the false-negative problem during training. Experiments on two real-world KGs demonstrate the effectiveness of our method. The source code and data of this paper can be obtained from https://github.com/CCIIPLab/CET.
The NiuTrans System for the WMT21 Efficiency Task
Authors: Chenglong Wang, Chi Hu, Yongyu Mu, Zhongxiang Yan, Siming Wu, Minyi Hu, Hang Cao, Bei Li, Ye Lin, Tong Xiao, Jingbo Zhu
Date: 2021-09-16 00:00:00
Tasks: GPU, Knowledge Distillation, Translation

This paper describes the NiuTrans system for the WMT21 translation efficiency task (http://statmt.org/wmt21/efficiency-task.html). Following last year's work, we explore various techniques to improve efficiency while maintaining translation quality. We investigate the combinations of lightweight Transformer architectures and knowledge distillation strategies. Also, we improve the translation efficiency with graph optimization, low precision, dynamic batching, and parallel pre/post-processing. Our system can translate 247,000 words per second on an NVIDIA A100, being 3$\times$ faster than last year's system. Our system is the fastest and has the lowest memory consumption on the GPU-throughput track. The code, model, and pipeline will be available at NiuTrans.NMT (https://github.com/NiuTrans/NiuTrans.NMT).
The NiuTrans System for WNGT 2020 Efficiency Task
Authors: Chi Hu, Bei Li, Ye Lin, Yinqiao Li, Yanyang Li, Chenglong Wang, Tong Xiao, Jingbo Zhu
Date: 2021-09-16 00:00:00
Proceeding: WS 2020 7
Tasks: Decoder, Knowledge Distillation, Machine Translation, Model Compression, Translation

This paper describes the submissions of the NiuTrans Team to the WNGT 2020 Efficiency Shared Task. We focus on the efficient implementation of deep Transformer models \cite{wang-etal-2019-learning, li-etal-2019-niutrans} using NiuTensor (https://github.com/NiuTrans/NiuTensor), a flexible toolkit for NLP tasks. We explored the combination of deep encoder and shallow decoder in Transformer models via model compression and knowledge distillation. The neural machine translation decoding also benefits from FP16 inference, attention caching, dynamic batching, and batch pruning. Our systems achieve promising results in both translation quality and efficiency, e.g., our fastest system can translate more than 40,000 tokens per second with an RTX 2080 Ti while maintaining 42.9 BLEU on \textit{newstest2018}. The code, models, and docker images are available at NiuTrans.NMT (https://github.com/NiuTrans/NiuTrans.NMT).
Locating Language-Specific Information in Contextualized Embeddings
Authors: Sheng Liang, Philipp Dufter, Hinrich Schütze
Date: 2021-09-16 00:00:00

Multilingual pretrained language models (MPLMs) exhibit multilinguality and are well suited for transfer across languages. Most MPLMs are trained in an unsupervised fashion and the relationship between their objective and multilinguality is unclear. More specifically, the question whether MPLM representations are language-agnostic or they simply interleave well with learned task prediction heads arises. In this work, we locate language-specific information in MPLMs and identify its dimensionality and the layers where this information occurs. We show that language-specific information is scattered across many dimensions, which can be projected into a linear subspace. Our study contributes to a better understanding of MPLM representations, going beyond treating them as unanalyzable blobs of information.
Automatic Error Type Annotation for Arabic
Authors: Riadh Belkebir, Nizar Habash
Date: 2021-09-16 00:00:00
Proceeding: CoNLL (EMNLP) 2021 11
Tasks: Grammatical Error Correction, Vocal Bursts Type Prediction

We present ARETA, an automatic error type annotation system for Modern Standard Arabic. We design ARETA to address Arabic's morphological richness and orthographic ambiguity. We base our error taxonomy on the Arabic Learner Corpus (ALC) Error Tagset with some modifications. ARETA achieves a performance of 85.8% (micro average F1 score) on a manually annotated blind test portion of ALC. We also demonstrate ARETA's usability by applying it to a number of submissions from the QALB 2014 shared task for Arabic grammatical error correction. The resulting analyses give helpful insights on the strengths and weaknesses of different submissions, which is more useful than the opaque M2 scoring metrics used in the shared task. ARETA employs a large Arabic morphological analyzer, but is completely unsupervised otherwise. We make ARETA publicly available.
MeLT: Message-Level Transformer with Masked Document Representations as Pre-Training for Stance Detection
Authors: Matthew Matero, Nikita Soni, Niranjan Balasubramanian, H. Andrew Schwartz
Date: 2021-09-16 00:00:00
Proceeding: Findings (EMNLP) 2021 11
Tasks: Attribute, Language Modeling, Language Modelling, Masked Language Modeling, Stance Detection

Much of natural language processing is focused on leveraging large capacity language models, typically trained over single messages with a task of predicting one or more tokens. However, modeling human language at higher-levels of context (i.e., sequences of messages) is under-explored. In stance detection and other social media tasks where the goal is to predict an attribute of a message, we have contextual data that is loosely semantically connected by authorship. Here, we introduce Message-Level Transformer (MeLT) -- a hierarchical message-encoder pre-trained over Twitter and applied to the task of stance prediction. We focus on stance prediction as a task benefiting from knowing the context of the message (i.e., the sequence of previous messages). The model is trained using a variant of masked-language modeling; where instead of predicting tokens, it seeks to generate an entire masked (aggregated) message vector via reconstruction loss. We find that applying this pre-trained masked message-level transformer to the downstream task of stance detection achieves F1 performance of 67%.
A Survey of Online Hate Speech through the Causal Lens
Authors: Antigoni-Maria Founta, Lucia Specia
Date: 2021-09-16 00:00:00
Proceeding: EMNLP (CINLP) 2021 11
Tasks: Causal Inference, Survey

The societal issue of digital hostility has previously attracted a lot of attention. The topic counts an ample body of literature, yet remains prominent and challenging as ever due to its subjective nature. We posit that a better understanding of this problem will require the use of causal inference frameworks. This survey summarises the relevant research that revolves around estimations of causal effects related to online hate speech. Initially, we provide an argumentation as to why re-establishing the exploration of hate speech in causal terms is of the essence. Following that, we give an overview of the leading studies classified with respect to the direction of their outcomes, as well as an outline of all related research, and a summary of open research problems that can influence future work on the topic.
Revisiting Tri-training of Dependency Parsers
Authors: Joachim Wagner, Jennifer Foster
Date: 2021-09-16 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Dependency Parsing, Word Embeddings

We compare two orthogonal semi-supervised learning techniques, namely tri-training and pretrained word embeddings, in the task of dependency parsing. We explore language-specific FastText and ELMo embeddings and multilingual BERT embeddings. We focus on a low resource scenario as semi-supervised learning can be expected to have the most impact here. Based on treebank size and available ELMo models, we select Hungarian, Uyghur (a zero-shot language for mBERT) and Vietnamese. Furthermore, we include English in a simulated low-resource setting. We find that pretrained word embeddings make more effective use of unlabelled data than tri-training but that the two approaches can be successfully combined.
Does Summary Evaluation Survive Translation to Other Languages?
Authors: Spencer Braun, Oleg Vasilyev, Neslihan Iskender, John Bohannon
Date: 2021-09-16 00:00:00
Proceeding: NAACL 2022 7
Tasks: Machine Translation, Translation

The creation of a quality summarization dataset is an expensive, time-consuming effort, requiring the production and evaluation of summaries by both trained humans and machines. If such effort is made in one language, it would be beneficial to be able to use it in other languages without repeating human annotations. To investigate how much we can trust machine translation of such a dataset, we translate the English dataset SummEval to seven languages and compare performance across automatic evaluation measures. We explore equivalence testing as the appropriate statistical paradigm for evaluating correlations between human and automated scoring of summaries. While we find some potential for dataset reuse in languages similar to the source, most summary evaluation methods are not found to be statistically equivalent across translations.
Phrase Retrieval Learns Passage Retrieval, Too
Authors: Jinhyuk Lee, Alexander Wettig, Danqi Chen
Date: 2021-09-16 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Entity Linking, Open-Domain Question Answering, Passage Retrieval, Quantization, Question Answering, Retrieval, slot-filling, Slot Filling

Dense retrieval methods have shown great promise over sparse retrieval methods in a range of NLP problems. Among them, dense phrase retrieval-the most fine-grained retrieval unit-is appealing because phrases can be directly used as the output for question answering and slot filling tasks. In this work, we follow the intuition that retrieving phrases naturally entails retrieving larger text blocks and study whether phrase retrieval can serve as the basis for coarse-level retrieval including passages and documents. We first observe that a dense phrase-retrieval system, without any retraining, already achieves better passage retrieval accuracy (+3-5% in top-5 accuracy) compared to passage retrievers, which also helps achieve superior end-to-end QA performance with fewer passages. Then, we provide an interpretation for why phrase-level supervision helps learn better fine-grained entailment compared to passage-level supervision, and also show that phrase retrieval can be improved to achieve competitive performance in document-retrieval tasks such as entity linking and knowledge-grounded dialogue. Finally, we demonstrate how phrase filtering and vector quantization can reduce the size of our index by 4-10x, making dense phrase retrieval a practical and versatile solution in multi-granularity retrieval.
An influencer-based approach to understanding radical right viral tweets
Authors: Laila Sprejer, Helen Margetts, Kleber Oliveira, David O'Sullivan, Bertie Vidgen
Date: 2021-09-15 00:00:00

Radical right influencers routinely use social media to spread highly divisive, disruptive and anti-democratic messages. Assessing and countering the challenge that such content poses is crucial for ensuring that online spaces remain open, safe and accessible. Previous work has paid little attention to understanding factors associated with radical right content that goes viral. We investigate this issue with a new dataset ROT which provides insight into the content, engagement and followership of a set of 35 radical right influencers. It includes over 50,000 original entries and over 40 million retweets, quotes, replies and mentions. We use a multilevel model to measure engagement with tweets, which are nested in each influencer. We show that it is crucial to account for the influencer-level structure, and find evidence of the importance of both influencer- and content-level factors, including the number of followers each influencer has, the type of content (original posts, quotes and replies), the length and toxicity of content, and whether influencers request retweets. We make ROT available for other researchers to use.