mtybilly/Instruct2DS huggingface dataset:
GFST: Gender-Filtered Self-Training for More Accurate Gender in Translation
Authors: Prafulla Kumar Choubey, Anna Currey, Prashant Mathur, Georgiana Dinu
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Targeted evaluations have found that machine translation systems often output incorrect gender in translations, even when the gender is clear from context. Furthermore, these incorrectly gendered translations have the potential to reflect or amplify social biases. We propose gender-filtered self-training (GFST) to improve gender translation accuracy on unambiguously gendered inputs. Our GFST approach uses a source monolingual corpus and an initial model to generate gender-specific pseudo-parallel corpora which are then filtered and added to the training data. We evaluate GFST on translation from English into five languages, finding that it improves gender accuracy without damaging generic quality. We also show the viability of GFST on several experimental settings, including re-training from scratch, fine-tuning, controlling the gender balance of the data, forward translation, and back-translation.
“Wikily” Supervised Neural Translation Tailored to Cross-Lingual Tasks
Authors: Mohammad Sadegh Rasooli, Chris Callison-Burch, Derry Tanti Wijaya
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: We present a simple but effective approach for leveraging Wikipedia for neural machine translation as well as cross-lingual tasks of image captioning and dependency parsing without using any direct supervision from external parallel data or supervised models in the target language. We show that first sentences and titles of linked Wikipedia pages, as well as cross-lingual image captions, are strong signals for a seed parallel data to extract bilingual dictionaries and cross-lingual word embeddings for mining parallel text from Wikipedia. Our final model achieves high BLEU scores that are close to or sometimes higher than strong \textitsupervised baselines in low-resource languages; e.g. supervised BLEU of 4.0 versus 12.1 from our model in English-to-Kazakh. Moreover, we tailor our \textitwikily translation models to unsupervised image captioning, and cross-lingual dependency parser transfer. In image captioning, we train a multi-tasking machine translation and image captioning pipeline for Arabic and English from which the Arabic training data is a \textitwikily translation of the English captioning data. Our captioning results on Arabic are slightly \textitbetter than that of its supervised model. In dependency parsing, we translate a large amount of monolingual text, and use it as an artificial training data in an \textitannotation projection framework. We show that our model outperforms recent work on cross-lingual transfer of dependency parsers.
mT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs
Authors: Zewen Chi, Li Dong, Shuming Ma, Shaohan Huang, Saksham Singhal, Xian-Ling Mao, Heyan Huang, Xia Song, Furu Wei
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Multilingual T5 pretrains a sequence-to-sequence model on massive monolingual texts, which has shown promising results on many cross-lingual tasks. In this paper, we improve multilingual text-to-text transfer Transformer with translation pairs (mT6). Specifically, we explore three cross-lingual text-to-text pre-training tasks, namely, machine translation, translation pair span corruption, and translation span corruption. In addition, we propose a partially non-autoregressive objective for text-to-text pre-training. We evaluate the methods on seven multilingual benchmark datasets, including sentence classification, named entity recognition, question answering, and abstractive summarization. Experimental results show that the proposed mT6 improves cross-lingual transferability over mT5.
Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training
Authors: Kuan-Hao Huang, Wasi Ahmad, Nanyun Peng, Kai-Wei Chang
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Pre-trained multilingual language encoders, such as multilingual BERT and XLM-R, show great potential for zero-shot cross-lingual transfer. However, these multilingual encoders do not precisely align words and phrases across languages. Especially, learning alignments in the multilingual embedding space usually requires sentence-level or word-level parallel corpora, which are expensive to be obtained for low-resource languages. An alternative is to make the multilingual encoders more robust; when fine-tuning the encoder using downstream task, we train the encoder to tolerate noise in the contextual embedding spaces such that even if the representations of different languages are not aligned well, the model can still achieve good performance on zero-shot cross-lingual transfer. In this work, we propose a learning strategy for training robust models by drawing connections between adversarial examples and the failure cases of zero-shot cross-lingual transfer. We adopt two widely used robust training methods, adversarial training and randomized smoothing, to train the desired robust model. The experimental results demonstrate that robust training improves zero-shot cross-lingual transfer on text classification tasks. The improvement is more significant in the generalized cross-lingual transfer setting, where the pair of input sentences belong to two different languages.
Speechformer: Reducing Information Loss in Direct Speech Translation
Authors: Sara Papi, Marco Gaido, Matteo Negri, Marco Turchi
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Transformer-based models have gained increasing popularity achieving state-of-the-art performance in many research fields including speech translation. However, Transformer`s quadratic complexity with respect to the input sequence length prevents its adoption as is with audio signals, which are typically represented by long sequences. Current solutions resort to an initial sub-optimal compression based on a fixed sampling of raw audio features. Therefore, potentially useful linguistic information is not accessible to higher-level layers in the architecture. To solve this issue, we propose Speechformer, an architecture that, thanks to reduced memory usage in the attention layers, avoids the initial lossy compression and aggregates information only at a higher level according to more informed linguistic criteria. Experiments on three language pairs (en\textrightarrowde/es/nl) show the efficacy of our solution, with gains of up to 0.8 BLEU on the standard MuST-C corpus and of up to 4.0 BLEU in a low resource scenario.
Is “moby dick” a Whale or a Bird? Named Entities and Terminology in Speech Translation
Authors: Marco Gaido, Susana Rodríguez, Matteo Negri, Luisa Bentivogli, Marco Turchi
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Automatic translation systems are known to struggle with rare words. Among these, named entities (NEs) and domain-specific terms are crucial, since errors in their translation can lead to severe meaning distortions. Despite their importance, previous speech translation (ST) studies have neglected them, also due to the dearth of publicly available resources tailored to their specific evaluation. To fill this gap, we i) present the first systematic analysis of the behavior of state-of-the-art ST systems in translating NEs and terminology, and ii) release NEuRoparl-ST, a novel benchmark built from European Parliament speeches annotated with NEs and terminology. Our experiments on the three language directions covered by our benchmark (en\textrightarrowes/fr/it) show that ST systems correctly translate 75--80% of terms and 65--70% of NEs, with very low performance (37--40%) on person names.
HintedBT: Augmenting Back-Translation with Quality and Transliteration Hints
Authors: Sahana Ramnath, Melvin Johnson, Abhirut Gupta, Aravindan Raghuveer
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Back-translation (BT) of target monolingual corpora is a widely used data augmentation strategy for neural machine translation (NMT), especially for low-resource language pairs. To improve effectiveness of the available BT data, we introduce HintedBT---a family of techniques which provides hints (through tags) to the encoder and decoder. First, we propose a novel method of using both high and low quality BT data by providing hints (as source tags on the encoder) to the model about the quality of each source-target pair. We don`t filter out low quality data but instead show that these hints enable the model to learn effectively from noisy data. Second, we address the problem of predicting whether a source token needs to be translated or transliterated to the target language, which is common in cross-script translation tasks (i.e., where source and target do not share the written script). For such cases, we propose training the model with additional hints (as target tags on the decoder) that provide information about the operation required on the source (translation or both translation and transliteration). We conduct experiments and detailed analyses on standard WMT benchmarks for three cross-script low/medium-resource language pairs: Hindi,Gujarati,Tamil-to-English. Our methods compare favorably with five strong and well established baselines. We show that using these hints, both separately and together, significantly improves translation quality and leads to state-of-the-art performance in all three language pairs in corresponding bilingual settings.
Translation-based Supervision for Policy Generation in Simultaneous Neural Machine Translation
Authors: Ashkan Alinejad, Hassan S. Shavarani, Anoop Sarkar
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: In simultaneous machine translation, finding an agent with the optimal action sequence of reads and writes that maintain a high level of translation quality while minimizing the average lag in producing target tokens remains an extremely challenging problem. We propose a novel supervised learning approach for training an agent that can detect the minimum number of reads required for generating each target token by comparing simultaneous translations against full-sentence translations during training to generate oracle action sequences. These oracle sequences can then be used to train a supervised model for action generation at inference time. Our approach provides an alternative to current heuristic methods in simultaneous translation by introducing a new training objective, which is easier to train than previous attempts at training the agent using reinforcement learning techniques for this task. Our experimental results show that our novel training method for action generation produces much higher quality translations while minimizing the average lag in simultaneous translation.
Nearest Neighbour Few-Shot Learning for Cross-lingual Classification
Authors: M Saiful Bari, Batool Haider, Saab Mansour
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Even though large pre-trained multilingual models (e.g. mBERT, XLM-R) have led to significant performance gains on a wide range of cross-lingual NLP tasks, success on many downstream tasks still relies on the availability of sufficient annotated data. Traditional fine-tuning of pre-trained models using only a few target samples can cause over-fitting. This can be quite limiting as most languages in the world are under-resourced. In this work, we investigate cross-lingual adaptation using a simple nearest-neighbor few-shot ($<15$ samples) inference technique for classification tasks. We experiment using a total of 16 distinct languages across two NLP tasks- XNLI and PAWS-X. Our approach consistently improves traditional fine-tuning using only a handful of labeled samples in target locales. We also demonstrate its generalization capability across tasks.
Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation
Authors: Mozhdeh Gheini, Xiang Ren, Jonathan May
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: We study the power of cross-attention in the Transformer architecture within the context of transfer learning for machine translation, and extend the findings of studies into cross-attention when training from scratch. We conduct a series of experiments through fine-tuning a translation model on data where either the source or target language has changed. These experiments reveal that fine-tuning only the cross-attention parameters is nearly as effective as fine-tuning all parameters (i.e., the entire translation model). We provide insights into why this is the case and observe that limiting fine-tuning in this manner yields cross-lingually aligned embeddings. The implications of this finding for researchers and practitioners include a mitigation of catastrophic forgetting, the potential for zero-shot translation, and the ability to extend machine translation models to several new language pairs with reduced parameter storage overhead.
Effects of Parameter Norm Growth During Transformer Training: Inductive Bias from Gradient Descent
Authors: William Merrill, Vivek Ramanujan, Yoav Goldberg, Roy Schwartz, Noah A. Smith
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: The capacity of neural networks like the widely adopted transformer is known to be very high. Evidence is emerging that they learn successfully due to inductive bias in the training routine, typically a variant of gradient descent (GD). To better understand this bias, we study the tendency for transformer parameters to grow in magnitude ($\ell_2$ norm) during training, and its implications for the emergent representations within self attention layers. Empirically, we document norm growth in the training of transformer language models, including T5 during its pretraining. As the parameters grow in magnitude, we prove that the network approximates a discretized network with saturated activation functions. Such “saturated” networks are known to have a reduced capacity compared to the full network family that can be described in terms of formal languages and automata. Our results suggest saturation is a new characterization of an inductive bias implicit in GD of particular interest for NLP. We leverage the emergent discrete structure in a saturated transformer to analyze the role of different attention heads, finding that some focus locally on a small number of positions, while other heads compute global averages, allowing counting. We believe understanding the interplay between these two capabilities may shed further light on the structure of computation within large transformers.
Foreseeing the Benefits of Incidental Supervision
Authors: Hangfeng He, Mingyuan Zhang, Qiang Ning, Dan Roth
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Real-world applications often require improved models by leveraging *a range of cheap incidental supervision signals*. These could include partial labels, noisy labels, knowledge-based constraints, and cross-domain or cross-task annotations -- all having statistical associations with gold annotations but not exactly the same. However, we currently lack a principled way to measure the benefits of these signals to a given target task, and the common practice of evaluating these benefits is through exhaustive experiments with various models and hyperparameters. This paper studies whether we can, *in a single framework, quantify the benefits of various types of incidental signals for a given target task without going through combinatorial experiments*. We propose a unified PAC-Bayesian motivated informativeness measure, PABI, that characterizes the uncertainty reduction provided by incidental supervision signals. We demonstrate PABI`s effectiveness by quantifying the value added by various types of incidental signals to sequence tagging tasks. Experiments on named entity recognition (NER) and question answering (QA) show that PABI`s predictions correlate well with learning performance, providing a promising way to determine, ahead of learning, which supervision signals would be beneficial.
Competency Problems: On Finding and Removing Artifacts in Language Data
Authors: Matt Gardner, William Merrill, Jesse Dodge, Matthew Peters, Alexis Ross, Sameer Singh, Noah A. Smith
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Much recent work in NLP has documented dataset artifacts, bias, and spurious correlations between input features and output labels. However, how to tell which features have “spurious” instead of legitimate correlations is typically left unspecified. In this work we argue that for complex language understanding tasks, all simple feature correlations are spurious, and we formalize this notion into a class of problems which we call competency problems. For example, the word “amazing” on its own should not give information about a sentiment label independent of the context in which it appears, which could include negation, metaphor, sarcasm, etc. We theoretically analyze the difficulty of creating data for competency problems when human bias is taken into account, showing that realistic datasets will increasingly deviate from competency problems as dataset size increases. This analysis gives us a simple statistical test for dataset artifacts, which we use to show more subtle biases than were described in prior work, including demonstrating that models are inappropriately affected by these less extreme biases. Our theoretical treatment of this problem also allows us to analyze proposed solutions, such as making local edits to dataset instances, and to give recommendations for future data collection and model design efforts that target competency problems.
Knowledge-Aware Meta-learning for Low-Resource Text Classification
Authors: Huaxiu Yao, Ying-xin Wu, Maruan Al-Shedivat, Eric Xing
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Meta-learning has achieved great success in leveraging the historical learned knowledge to facilitate the learning process of the new task. However, merely learning the knowledge from the historical tasks, adopted by current meta-learning algorithms, may not generalize well to testing tasks when they are not well-supported by training tasks. This paper studies a low-resource text classification problem and bridges the gap between meta-training and meta-testing tasks by leveraging the external knowledge bases. Specifically, we propose KGML to introduce additional representation for each sentence learned from the extracted sentence-specific knowledge graph. The extensive experiments on three datasets demonstrate the effectiveness of KGML under both supervised adaptation and unsupervised adaptation settings.
Sentence Bottleneck Autoencoders from Transformer Language Models
Authors: Ivan Montero, Nikolaos Pappas, Noah A. Smith
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Representation learning for text via pretraining a language model on a large corpus has become a standard starting point for building NLP systems. This approach stands in contrast to autoencoders, also trained on raw text, but with the objective of learning to encode each input as a vector that allows full reconstruction. Autoencoders are attractive because of their latent space structure and generative properties. We therefore explore the construction of a sentence-level autoencoder from a pretrained, frozen transformer language model. We adapt the masked language modeling objective as a generative, denoising one, while only training a sentence bottleneck and a single-layer modified transformer decoder. We demonstrate that the sentence representations discovered by our model achieve better quality than previous methods that extract representations from pretrained transformers on text similarity tasks, style transfer (an example of controlled generation), and single-sentence classification tasks in the GLUE benchmark, while using fewer parameters than large pretrained models.
Efficient Contrastive Learning via Novel Data Augmentation and Curriculum Learning
Authors: Seonghyeon Ye, Jiseon Kim, Alice Oh
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: We introduce EfficientCL, a memory-efficient continual pretraining method that applies contrastive learning with novel data augmentation and curriculum learning. For data augmentation, we stack two types of operation sequentially: cutoff and PCA jittering. While pretraining steps proceed, we apply curriculum learning by incrementing the augmentation degree for each difficulty step. After data augmentation is finished, contrastive learning is applied on projected embeddings of original and augmented examples. When finetuned on GLUE benchmark, our model outperforms baseline models, especially for sentence-level tasks. Additionally, this improvement is capable with only 70% of computational memory compared to the baseline model.
CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation
Authors: Wenchang Ma, Ryuichi Takanobu, Minlie Huang
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Growing interests have been attracted in Conversational Recommender Systems (CRS), which explore user preference through conversational interactions in order to make appropriate recommendation. However, there is still a lack of ability in existing CRS to (1) traverse multiple reasoning paths over background knowledge to introduce relevant items and attributes, and (2) arrange selected entities appropriately under current system intents to control response generation. To address these issues, we propose CR-Walker in this paper, a model that performs tree-structured reasoning on a knowledge graph, and generates informative dialog acts to guide language generation. The unique scheme of tree-structured reasoning views the traversed entity at each hop as part of dialog acts to facilitate language generation, which links how entities are selected and expressed. Automatic and human evaluations show that CR-Walker can arrive at more accurate recommendation, and generate more informative and engaging responses.
DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization
Authors: Zeqiu Wu, Bo-Ru Lu, Hannaneh Hajishirzi, Mari Ostendorf
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Identifying relevant knowledge to be used in conversational systems that are grounded in long documents is critical to effective response generation. We introduce a knowledge identification model that leverages the document structure to provide dialogue-contextualized passage encodings and better locate knowledge relevant to the conversation. An auxiliary loss captures the history of dialogue-document connections. We demonstrate the effectiveness of our model on two document-grounded conversational datasets and provide analyses showing generalization to unseen documents and long dialogue contexts.
Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text
Authors: Christopher Clark, Jordi Salvador, Dustin Schwenk, Derrick Bonafilia, Mark Yatskar, Eric Kolve, Alvaro Herrasti, Jonghyun Choi, Sachin Mehta, Sam Skjonsberg, Carissa Schoenick, Aaron Sarnat, Hannaneh Hajishirzi, Aniruddha Kembhavi, Oren Etzioni, Ali Farhadi
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Communicating with humans is challenging for AIs because it requires a shared understanding of the world, complex semantics (e.g., metaphors or analogies), and at times multi-modal gestures (e.g., pointing with a finger, or an arrow in a diagram). We investigate these challenges in the context of Iconary, a collaborative game of drawing and guessing based on Pictionary, that poses a novel challenge for the research community. In Iconary, a Guesser tries to identify a phrase that a Drawer is drawing by composing icons, and the Drawer iteratively revises the drawing to help the Guesser in response. This back-and-forth often uses canonical scenes, visual metaphor, or icon compositions to express challenging words, making it an ideal test for mixing language and visual/symbolic communication in AI. We propose models to play Iconary and train them on over 55,000 games between human players. Our models are skillful players and are able to employ world knowledge in language models to play with words unseen during training.
Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems
Authors: Fei Mi, Wanhao Zhou, Lingjing Kong, Fengyu Cai, Minlie Huang, Boi Faltings
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: As the labeling cost for different modules in task-oriented dialog (ToD) systems is expensive, a major challenge is to train different modules with the least amount of labeled data. Recently, large-scale pre-trained language models, have shown promising results for few-shot learning in ToD. In this paper, we devise a self-training approach to utilize the abundant unlabeled dialog data to further improve state-of-the-art pre-trained models in few-shot learning scenarios for ToD systems. Specifically, we propose a self-training approach that iteratively labels the most confident unlabeled data to train a stronger Student model. Moreover, a new text augmentation technique (GradAug) is proposed to better train the Student by replacing non-crucial tokens using a masked language model. We conduct extensive experiments and present analyses on four downstream tasks in ToD, including intent classification, dialog state tracking, dialog act prediction, and response selection. Empirical results demonstrate that the proposed self-training approach consistently improves state-of-the-art pre-trained models (BERT, ToD-BERT) when only a small number of labeled data are available.
Contextual Rephrase Detection for Reducing Friction in Dialogue Systems
Authors: Zhuoyi Wang, Saurabh Gupta, Jie Hao, Xing Fan, Dingcheng Li, Alexander Hanbo Li, Chenlei Guo
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: For voice assistants like Alexa, Google Assistant, and Siri, correctly interpreting users' intentions is of utmost importance. However, users sometimes experience friction with these assistants, caused by errors from different system components or user errors such as slips of the tongue. Users tend to rephrase their queries until they get a satisfactory response. Rephrase detection is used to identify the rephrases and has long been treated as a task with pairwise input, which does not fully utilize the contextual information (e.g. users' implicit feedback). To this end, we propose a contextual rephrase detection model ContReph to automatically identify rephrases from multi-turn dialogues. We showcase how to leverage the dialogue context and user-agent interaction signals, including the user`s implicit feedback and the time gap between different turns, which can help significantly outperform the pairwise rephrase detection models.
Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning
Authors: Jianguo Zhang, Trung Bui, Seunghyun Yoon, Xiang Chen, Zhiwei Liu, Congying Xia, Quan Hung Tran, Walter Chang, Philip Yu
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: In this work, we focus on a more challenging few-shot intent detection scenario where many intents are fine-grained and semantically similar. We present a simple yet effective few-shot intent detection schema via contrastive pre-training and fine-tuning. Specifically, we first conduct self-supervised contrastive pre-training on collected intent datasets, which implicitly learns to discriminate semantically similar utterances without using any labels. We then perform few-shot intent detection together with supervised contrastive learning, which explicitly pulls utterances from the same intent closer and pushes utterances across different intents farther. Experimental results show that our proposed method achieves state-of-the-art performance on three challenging intent detection datasets under 5-shot and 10-shot settings.
“It doesn`t look good for a date”: Transforming Critiques into Preferences for Conversational Recommendation Systems
Authors: Victor Bursztyn, Jennifer Healey, Nedim Lipka, Eunyee Koh, Doug Downey, Larry Birnbaum
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Conversations aimed at determining good recommendations are iterative in nature. People often express their preferences in terms of a critique of the current recommendation (e.g., “It doesn`t look good for a date”), requiring some degree of common sense for a preference to be inferred. In this work, we present a method for transforming a user critique into a positive preference (e.g., “I prefer more romantic”) in order to retrieve reviews pertaining to potentially better recommendations (e.g., “Perfect for a romantic dinner”). We leverage a large neural language model (LM) in a few-shot setting to perform critique-to-preference transformation, and we test two methods for retrieving recommendations: one that matches embeddings, and another that fine-tunes an LM for the task. We instantiate this approach in the restaurant domain and evaluate it using a new dataset of restaurant critiques. In an ablation study, we show that utilizing critique-to-preference transformation improves recommendations, and that there are at least three general cases that explain this improved performance.
AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions
Authors: Haoran Ding, Xiao Luo
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Keyword or keyphrase extraction is to identify words or phrases presenting the main topics of a document. This paper proposes the AttentionRank, a hybrid attention model, to identify keyphrases from a document in an unsupervised manner. AttentionRank calculates self-attention and cross-attention using a pre-trained language model. The self-attention is designed to determine the importance of a candidate within the context of a sentence. The cross-attention is calculated to identify the semantic relevance between a candidate and sentences within a document. We evaluate the AttentionRank on three publicly available datasets against seven baselines. The results show that the AttentionRank is an effective and robust unsupervised keyphrase extraction model on both long and short documents. Source code is available on Github.
Unsupervised Relation Extraction: A Variational Autoencoder Approach
Authors: Chenhan Yuan, Hoda Eldardiry
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Unsupervised relation extraction works by clustering entity pairs that have the same relations in the text. Some existing variational autoencoder (VAE)-based approaches train the relation extraction model as an encoder that generates relation classifications. A decoder is trained along with the encoder to reconstruct the encoder input based on the encoder-generated relation classifications. These classifications are a latent variable so they are required to follow a pre-defined prior distribution which results in unstable training. We propose a VAE-based unsupervised relation extraction technique that overcomes this limitation by using the classifications as an intermediate variable instead of a latent variable. Specifically, classifications are conditioned on sentence input, while the latent variable is conditioned on both the classifications and the sentence input. This allows our model to connect the decoder with the encoder without putting restrictions on the classification distribution; which improves training stability. Our approach is evaluated on the NYT dataset and outperforms state-of-the-art methods.
Robust Retrieval Augmented Generation for Zero-shot Slot Filling
Authors: Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Alfio Gliozzo
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Automatically inducing high quality knowledge graphs from a given collection of documents still remains a challenging problem in AI. One way to make headway for this problem is through advancements in a related task known as slot filling. In this task, given an entity query in form of [Entity, Slot, ?], a system is asked to \textquoteleftfill' the slot by generating or extracting the missing value exploiting evidence extracted from relevant passage(s) in the given document collection. The recent works in the field try to solve this task in an end-to-end fashion using retrieval-based language models. In this paper, we present a novel approach to zero-shot slot filling that extends dense passage retrieval with hard negatives and robust training procedures for retrieval augmented generation models. Our model reports large improvements on both T-REx and zsRE slot filling datasets, improving both passage retrieval and slot value generation, and ranking at the top-1 position in the KILT leaderboard. Moreover, we demonstrate the robustness of our system showing its domain adaptation capability on a new variant of the TACRED dataset for slot filling, through a combination of zero/few-shot learning. We release the source code and pre-trained models.
Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction
Authors: Mahsa Yarmohammadi, Shijie Wu, Marc Marone, Haoran Xu, Seth Ebner, Guanghui Qin, Yunmo Chen, Jialiang Guo, Craig Harman, Kenton Murray, Aaron Steven White, Mark Dredze, Benjamin Van Durme
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Zero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other language, typically English. While the advance of pretrained multilingual encoders suggests an easy optimism of “train on English, run on any language”, we find through a thorough exploration and extension of techniques that a combination of approaches, both new and old, leads to better performance than any one cross-lingual strategy in particular. We explore techniques including data projection and self-training, and how different pretrained encoders impact them. We use English-to-Arabic IE as our initial example, demonstrating strong performance in this setting for event extraction, named entity recognition, part-of-speech tagging, and dependency parsing. We then apply data projection and self-training to three tasks across eight target languages. Because no single set of techniques performs the best across all tasks, we encourage practitioners to explore various configurations of the techniques described in this work when seeking to improve on zero-shot training.
Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies
Authors: Sunipa Dev, Masoud Monajatipoor, Anaelia Ovalle, Arjun Subramonian, Jeff Phillips, Kai-Wei Chang
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Gender is widely discussed in the context of language tasks and when examining the stereotypes propagated by language models. However, current discussions primarily treat gender as binary, which can perpetuate harms such as the cyclical erasure of non-binary gender identities. These harms are driven by model and dataset biases, which are consequences of the non-recognition and lack of understanding of non-binary genders in society. In this paper, we explain the complexity of gender and language around it, and survey non-binary persons to understand harms associated with the treatment of gender as binary in English language technologies. We also detail how current language representations (e.g., GloVe, BERT) capture and perpetuate these harms and related challenges that need to be acknowledged and addressed for representations to equitably encode gender information.
Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search
Authors: Jialu Wang, Yang Liu, Xin Wang
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Internet search affects people`s cognition of the world, so mitigating biases in search results and learning fair models is imperative for social good. We study a unique gender bias in image search in this work: the search images are often gender-imbalanced for gender-neutral natural language queries. We diagnose two typical image search models, the specialized model trained on in-domain datasets and the generalized representation model pre-trained on massive image and text data across the internet. Both models suffer from severe gender bias. Therefore, we introduce two novel debiasing approaches: an in-processing fair sampling method to address the gender imbalance issue for training models, and a post-processing feature clipping method base on mutual information to debias multimodal representations of pre-trained models. Extensive experiments on MS-COCO and Flickr30K benchmarks show that our methods significantly reduce the gender bias in image search models.
Style Pooling: Automatic Text Style Obfuscation for Improved Classification Fairness
Authors: Fatemehsadat Mireshghallah, Taylor Berg-Kirkpatrick
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Text style can reveal sensitive attributes of the author (e.g. age and race) to the reader, which can, in turn, lead to privacy violations and bias in both human and algorithmic decisions based on text. For example, the style of writing in job applications might reveal protected attributes of the candidate which could lead to bias in hiring decisions, regardless of whether hiring decisions are made algorithmically or by humans. We propose a VAE-based framework that obfuscates stylistic features of human-generated text through style transfer, by automatically re-writing the text itself. Critically, our framework operationalizes the notion of obfuscated style in a flexible way that enables two distinct notions of obfuscated style: (1) a minimal notion that effectively intersects the various styles seen in training, and (2) a maximal notion that seeks to obfuscate by adding stylistic features of all sensitive attributes to text, in effect, computing a union of styles. Our style-obfuscation framework can be used for multiple purposes, however, we demonstrate its effectiveness in improving the fairness of downstream classifiers. We also conduct a comprehensive study on style-pooling`s effect on fluency, semantic consistency, and attribute removal from text, in two and three domain style transfer.
Modeling Disclosive Transparency in NLP Application Descriptions
Authors: Michael Saxon, Sharon Levy, Xinyi Wang, Alon Albalak, William Yang Wang
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Broader disclosive transparency---truth and clarity in communication regarding the function of AI systems---is widely considered desirable. Unfortunately, it is a nebulous concept, difficult to both define and quantify. This is problematic, as previous work has demonstrated possible trade-offs and negative consequences to disclosive transparency, such as a confusion effect, where “too much information” clouds a reader`s understanding of what a system description means. Disclosive transparency`s subjective nature has rendered deep study into these problems and their remedies difficult. To improve this state of affairs, We introduce neural language model-based probabilistic metrics to directly model disclosive transparency, and demonstrate that they correlate with user and expert opinions of system transparency, making them a valid objective proxy. Finally, we demonstrate the use of these metrics in a pilot study quantifying the relationships between transparency, confusion, and user perceptions in a corpus of real NLP system descriptions.
Reconstruction Attack on Instance Encoding for Language Understanding
Authors: Shangyu Xie, Yuan Hong
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: A private learning scheme TextHide was recently proposed to protect the private text data during the training phase via so-called instance encoding. We propose a novel reconstruction attack to break TextHide by recovering the private training data, and thus unveil the privacy risks of instance encoding. We have experimentally validated the effectiveness of the reconstruction attack with two commonly-used datasets for sentence classification. Our attack would advance the development of privacy preserving machine learning in the context of natural language processing.
Fairness-aware Class Imbalanced Learning
Authors: Shivashankar Subramanian, Afshin Rahimi, Timothy Baldwin, Trevor Cohn, Lea Frermann
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Class imbalance is a common challenge in many NLP tasks, and has clear connections to bias, in that bias in training data often leads to higher accuracy for majority groups at the expense of minority groups. However there has traditionally been a disconnect between research on class-imbalanced learning and mitigating bias, and only recently have the two been looked at through a common lens. In this work we evaluate long-tail learning methods for tweet sentiment and occupation classification, and extend a margin-loss based approach with methods to enforce fairness. We empirically show through controlled experiments that the proposed approaches help mitigate both class imbalance and demographic biases.
CRYPTOGRU: Low Latency Privacy-Preserving Text Analysis With GRU
Authors: Bo Feng, Qian Lou, Lei Jiang, Geoffrey Fox
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Homomorphic encryption (HE) and garbled circuit (GC) provide the protection for users' privacy. However, simply mixing the HE and GC in RNN models suffer from long inference latency due to slow activation functions. In this paper, we present a novel hybrid structure of HE and GC gated recurrent unit (GRU) network, , for low-latency secure inferences. replaces computationally expensive GC-based $tanh$ with fast GC-based $ReLU$, and then quantizes $sigmoid$ and $ReLU$ to smaller bit-length to accelerate activations in a GRU. We evaluate with multiple GRU models trained on 4 public datasets. Experimental results show achieves top-notch accuracy and improves the secure inference latency by up to $138\times$ over one of the state-of-the-art secure networks on the Penn Treebank dataset.
Local Word Discovery for Interactive Transcription
Authors: William Lane, Steven Bird
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Human expertise and the participation of speech communities are essential factors in the success of technologies for low-resource languages. Accordingly, we propose a new computational task which is tuned to the available knowledge and interests in an Indigenous community, and which supports the construction of high quality texts and lexicons. The task is illustrated for Kunwinjku, a morphologically-complex Australian language. We combine a finite state implementation of a published grammar with a partial lexicon, and apply this to a noisy phone representation of the signal. We locate known lexemes in the signal and use the morphological transducer to build these out into hypothetical, morphologically-complex words for human validation. We show that applying a single iteration of this method results in a relative transcription density gain of 17%. Further, we find that 75% of breath groups in the test set receive at least one correct partial or full-word suggestion.
Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision
Authors: Mieradilijiang Maimaiti, Yang Liu, Yuanhang Zheng, Gang Chen, Kaiyu Huang, Ji Zhang, Huanbo Luan, Maosong Sun
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Recent state-of-the-art (SOTA) effective neural network methods and fine-tuning methods based on pre-trained models (PTM) have been used in Chinese word segmentation (CWS), and they achieve great results. However, previous works focus on training the models with the fixed corpus at every iteration. The intermediate generated information is also valuable. Besides, the robustness of the previous neural methods is limited by the large-scale annotated data. There are a few noises in the annotated corpus. Limited efforts have been made by previous studies to deal with such problems. In this work, we propose a self-supervised CWS approach with a straightforward and effective architecture. First, we train a word segmentation model and use it to generate the segmentation results. Then, we use a revised masked language model (MLM) to evaluate the quality of the segmentation results based on the predictions of the MLM. Finally, we leverage the evaluations to aid the training of the segmenter by improved minimum risk training. Experimental results show that our approach outperforms previous methods on 9 different CWS datasets with single criterion training and multiple criteria training and achieves better robustness.