mtybilly/Instruct2DS huggingface dataset:
Summarization as Indirect Supervision for Relation Extraction
Authors: I-Hung Hsu, Wenxuan Zhou, Mingyu Derek Ma, Muhao Chen Keming Lu
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Relation extraction (RE) models have been challenged by their reliance on training data with expensive annotations. Considering that summarization tasks aim at acquiring concise expressions of synoptical information from the longer context, these tasks naturally align with the objective of RE, i.e., extracting a kind of synoptical information that describes the relation of entity mentions. We present SuRE, which converts RE into a summarization formulation. SuRE leads to more precise and resource-efficient RE based on indirect supervision from summarization tasks. To achieve this goal, we develop sentence and relation conversion techniques that essentially bridge the formulation of summarization and RE tasks. We also incorporate constraint decoding techniques with Trie scoring to further enhance summarization-based RE with robust inference. Experiments on three RE datasets demonstrate the effectiveness of SuRE in both full-dataset and low-resource settings, showing that summarization is a promising source of indirect supervision signals to improve RE models.
DIGAT: Modeling News Recommendation with Dual-Graph Interaction
Authors: Jian Li, Hongru Wang, Xingshan Zeng, Kam-Fai Wong Zhiming Mao
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: News recommendation (NR) is essential for online news services. Existing NR methods typically adopt a news-user representation learning framework, facing two potential limitations. First, in news encoder, single candidate news encoding suffers from an insufficient semantic information problem. Second, existing graph-based NR methods are promising but lack effective news-user feature interaction, rendering the graph-based recommendation suboptimal. To overcome these limitations, we propose dual-interactive graph attention networks (DIGAT) consisting of news- and user-graph channels. In the news-graph channel, we enrich the semantics of single candidate news by incorporating the semantically relevant news information with a semantic-augmented graph (SAG). In the user-graph channel, multi-level user interests are represented with a news-topic graph. Most notably, we design a dual-graph interaction process to perform effective feature interaction between the news and user graphs, which facilitates accurate news-user representation matching. Experiment results on the benchmark dataset MIND show that DIGAT outperforms existing news recommendation methods. Further ablation studies and analyses validate the effectiveness of (1) semantic-augmented news graph modeling and (2) dual-graph interaction.
SMASH: Improving SMAll Language Models' Few-SHot Ability with Prompt-Based Distillation
Authors: Chang Liu, Kai Chen, Xi Wang, Dongyan Zhao Yueqian Wang
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Large-scale language models coupled with prompts have shown remarkable performance on few-shot learning. However, through systematic experiments, we find that the few-shot performance of small language models is poor, and using prompts on them brings fewer improvements than on larger ones. In this paper, we propose SMASH, an approach to improve SMAll language models' few-SHot ability by training on intermediate tasks before prompt-based fine-tuning on downstream tasks. We design intermediate tasks for sentence-pair tasks and sentiment classification tasks by creating training examples with prompt templates similar to downstream tasks using sentences sampled from a large-scale unsupervised corpus, and apply knowledge distillation to distill from outputs of larger pre-trained models as the training objective. We conduct extensive experiments and show that SMASH can make a 6-layer DistilRoBRETa-base achieve comparable performance on few-shot datasets with a 12-layer RoBERTa-base at a low cost.
Consecutive Question Generation via Dynamic Multitask Learning
Authors: Sujian Li, Xing Shi Yunji Li
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: In this paper, we propose the task of consecutive question generation (CQG), which generates a set of logically related question-answer pairs to understand a whole passage, with a comprehensive consideration of the aspects including accuracy, coverage, and informativeness.To achieve this, we first examine the four key elements of CQG, i.e., question, answer, rationale, and context history, and propose a novel dynamic multitask framework with one main task generating a question-answer pair, and four auxiliary tasks generating other elements. It directly helps the model generate good questions through both joint training and self-reranking. At the same time, to fully explore the worth-asking information in a given passage, we make use of the reranking losses to sample the rationales and search for the best question series globally.Finally, we measure our strategy by QA data augmentation and manual evaluation, as well as a novel application of generated question-answer pairs on DocNLI. We prove that our strategy can improve question generation significantly and benefit multiple related NLP tasks.
Subword Segmental Language Modelling for Nguni Languages
Authors: Jan Buys Francois Meyer
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Subwords have become the standard units of text in NLP, enabling efficient open-vocabulary models. With algorithms like byte-pair encoding (BPE), subword segmentation is viewed as a preprocessing step applied to the corpus before training. This can lead to sub-optimal segmentations for low-resource languages with complex morphologies. We propose a subword segmental language model (SSLM) that learns how to segment words while being trained for autoregressive language modelling. By unifying subword segmentation and language modelling, our model learns subwords that optimise LM performance. We train our model on the 4 Nguni languages of South Africa. These are low-resource agglutinative languages, so subword information is critical. As an LM, SSLM outperforms existing approaches such as BPE-based models on average across the 4 languages. Furthermore, it outperforms standard subword segmenters on unsupervised morphological segmentation. We also train our model as a word-level sequence model, resulting in an unsupervised morphological segmenter that outperforms existing methods by a large margin for all 4 languages. Our results show that learning subword segmentation is an effective alternative to existing subword segmenters, enabling the model to discover morpheme-like subwords that improve its LM capabilities.
Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning
Authors: Yuanxin Liu, Fandong Meng, Zheng Lin, Peng Fu, Yanan Cao, Weiping Wang, Jie Zhou Qingyi Si
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Models for Visual Question Answering (VQA) often rely on the spurious correlations, i.e., the language priors, that appear in the biased samples of training set, which make them brittle against the out-of-distribution (OOD) test data. Recent methods have achieved promising progress in overcoming this problem by reducing the impact of biased samples on model training. However, these models reveal a trade-off that the improvements on OOD data severely sacrifice the performance on the in-distribution (ID) data (which is dominated by the biased samples). Therefore, we propose a novel contrastive learning approach, MMBS, for building robust VQA models by Making the Most of Biased Samples. Specifically, we construct positive samples for contrastive learning by eliminating the information related to spurious correlation from the original training samples and explore several strategies to use the constructed positive samples for training. Instead of undermining the importance of biased samples in model training, our approach precisely exploits the biased samples for unbiased information that contributes to reasoning. The proposed method is compatible with various VQA backbones. We validate our contributions by achieving competitive performance on the OOD dataset VQA-CP v2 while preserving robust performance on the ID dataset VQA v2.
P3LM: Probabilistically Permuted Prophet Language Modeling for Generative Pre-Training
Authors: Yifan Wang, Ying Jiangyong, Yeyun Gong, Jing Zhao, Youzheng Wu, Xiaodong He Junwei Bao
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Conventional autoregressive left-to-right (L2R) sequence generation faces two issues during decoding: limited to unidirectional target sequence modeling, and constrained on strong local dependencies.To address the aforementioned problem, we propose P3LM, a probabilistically permuted prophet language model, which strengthens the modeling of bidirectional information and long token dependencies for sequence generation.Specifically, P3LM learns to generate tokens in permuted order upon an order-aware transformer decoder, as well as to generate the corresponding future N tokens with a multi-stream attention mechanism.Extensive experiments are conducted on the GLGE benchmark, which includes four datasets for summarization, two for question generation, one for conversational question answering, and one for dialog response generation, where P3LM achieves state-of-the-art results compared with strong publicly available generative pre-training methods.
Holistic Sentence Embeddings for Better Out-of-Distribution Detection
Authors: Xiaohan Bi, Rundong Gao, Xu Sun Sishuo Chen
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Detecting out-of-distribution (OOD) instances is significant for the safe deployment of NLP models. Among recent textual OOD detection works based on pretrained language models (PLMs), distance-based methods have shown superior performance. However, they estimate sample distance scores in the last-layer CLS embedding space and thus do not make full use of linguistic information underlying in PLMs. To address the issue, we propose to boost OOD detection by deriving more holistic sentence embeddings. On the basis of the observations that token averaging and layer combination contribute to improving OOD detection, we propose a simple embedding approach named Avg-Avg, which averages all token representations from each intermediate layer as the sentence embedding and significantly surpasses the state-of-the-art on a comprehensive suite of benchmarks by a 9.33% FAR95 margin. Furthermore, our analysis demonstrates that it indeed helps preserve general linguistic knowledge in fine-tuned PLMs and substantially benefits detecting background shifts. The simple yet effective embedding method can be applied to fine-tuned PLMs with negligible extra costs, providing a free gain in OOD detection. Our code is available at https://github.com/lancopku/Avg-Avg.
MuGER2: Multi-Granularity Evidence Retrieval and Reasoning for Hybrid Question Answering
Authors: Junwei Bao, Chaoqun Duan, Youzheng Wu, Xiaodong He, Tiejun Zhao Yingyao Wang
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Hybrid question answering (HQA) aims to answer questions over heterogeneous data, including tables and passages linked to table cells. The heterogeneous data can provide different granularity evidence to HQA models, e.t., column, row, cell, and link. Conventional HQA models usually retrieve coarse- or fine-grained evidence to reason the answer. Through comparison, we find that coarse-grained evidence is easier to retrieve but contributes less to the reasoner, while fine-grained evidence is the opposite. To preserve the advantage and eliminate the disadvantage of different granularity evidence, we propose MuGER2, a Multi-Granularity Evidence Retrieval and Reasoning approach. In evidence retrieval, a unified retriever is designed to learn the multi-granularity evidence from the heterogeneous data. In answer reasoning, an evidence selector is proposed to navigate the fine-grained evidence for the answer reader based on the learned multi-granularity evidence. Experiment results on the HybridQA dataset show that MuGER2 significantly boosts the HQA performance. Further ablation analysis verifies the effectiveness of both the retrieval and reasoning designs.
EntityCS: Improving Zero-Shot Cross-lingual Transfer with Entity-Centric Code Switching
Authors: Fenia Christopoulou, Ignacio Iacobacci Chenxi Whitehouse
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Accurate alignment between languages is fundamental for improving cross-lingual pre-trained language models (XLMs). Motivated by the natural phenomenon of code-switching (CS) in multilingual speakers, CS has been used as an effective data augmentation method that offers language alignment at word- or phrase-level, in contrast to sentence-level via parallel instances. Existing approaches either use dictionaries or parallel sentences with word-alignment to generate CS data by randomly switching words in a sentence. However, such methods can be suboptimal as dictionaries disregard semantics, and syntax might become invalid after random word switching. In this work, we propose EntityCS, a method that focuses on Entity-level Code-Switching to capture fine-grained cross-lingual semantics without corrupting syntax. We use Wikidata and the English Wikipedia to construct an entity-centric CS corpus by switching entities to their counterparts in other languages. We further propose entity-oriented masking strategies during intermediate model training on the EntityCS corpus for improving entity prediction. Evaluation of the trained models on four entity-centric downstream tasks shows consistent improvements over the baseline with a notable increase of 10% in Fact Retrieval. We release the corpus and models to assist research on code-switching and enriching XLMs with external knowledge.
MBTI Personality Prediction for Fictional Characters Using Movie Scripts
Authors: Xiangyang Mou, Mo Yu, Dakuo Wang, Jing Li, Jeffrey Stanton Yisi Sang
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: An NLP model that understands stories should be able to understand the characters in them. To support the development of neural models for this purpose, we construct a benchmark, Story2Personality. The task is to predict a movie character`s MBTI or Big 5 personality types based on the narratives of the character. Experiments show that our task is challenging for the existing text classification models, as none is able to largely outperform random guesses. We further proposed a multi-view model for personality prediction using both verbal and non-verbal descriptions, which gives improvement compared to using only verbal descriptions. The uniqueness and challenges in our dataset call for the development of narrative comprehension techniques from the perspective of understanding characters.
A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing
Authors: Tsutomu Hirao, Hidetaka Kamigaito, Manabu Okumura, Masaaki Nagata Naoki Kobayashi
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: To promote and further develop RST-style discourse parsing models, we need a strong baseline that can be regarded as a reference for reporting reliable experimental results. This paper explores a strong baseline by integrating existing simple parsing strategies, top-down and bottom-up, with various transformer-based pre-trained language models.The experimental results obtained from two benchmark datasets demonstrate that the parsing performance strongly relies on the pre-trained language models rather than the parsing strategies.In particular, the bottom-up parser achieves large performance gains compared to the current best parser when employing DeBERTa.We further reveal that language models with a span-masking scheme especially boost the parsing performance through our analysis within intra- and multi-sentential parsing, and nuclearity prediction.
Probing for Constituency Structure in Neural Language Models
Authors: Younes Samih, Laura Kallmeyer, Hassan Sajjad David Arps
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: In this paper, we investigate to which extent contextual neural language models (LMs) implicitly learn syntactic structure. More concretely, we focus on constituent structure as represented in the Penn Treebank (PTB). Using standard probing techniques based on diagnostic classifiers, we assess the accuracy of representing constituents of different categories within the neuron activations of a LM such as RoBERTa. In order to make sure that our probe focuses on syntactic knowledge and not on implicit semantic generalizations, we also experiment on a PTB version that is obtained by randomly replacing constituents with each other while keeping syntactic structure, i.e., a semantically ill-formed but syntactically well-formed version of the PTB. We find that 4 pretrained transfomer LMs obtain high performance on our probing tasks even on manipulated data, suggesting that semantic and syntactic knowledge in their representations can be separated and that constituency information is in fact learned by the LM. Moreover, we show that a complete constituency tree can be linearly separated from LM representations.
Table-To-Text generation and pre-training with TabT5
Authors: Julian Eisenschlos, Francesco Piccinno, Syrine Krichene, Yasemin Altun Ewa Andrejczuk
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Encoder-only transformer models have been successfully applied to different table understanding tasks, as in TAPAS. A major limitation of these architectures is that they are constrained to classification-like tasks such as cell selection or entailment detection. We present TabT5, an encoder-decoder model that generates natural language text based on tables and textual inputs. TabT5 overcomes the encoder-only limitation by incorporating a decoder component and leverages the input structure with table specific embeddings and pre-training. TabT5 achieves new state-of-the-art results on several domains, including spreadsheet formula prediction with a 15% increase in sequence accuracy, QA with a 2.5% increase in sequence accuracy and data-to-text generation with a 2.5% increase in BLEU.
A POMDP Dialogue Policy with 3-way Grounding and Adaptive Sensing for Learning through Communication
Authors: Alan Wagner, Rebecca Passonneau Maryam Zare
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Agents to assist with rescue, surgery, and similar activities could collaborate better with humans if they could learn new strategic behaviors through communication. We introduce a novel POMDP dialogue policy for learning from people. The policy has 3-way grounding of language in the shared physical context, the dialogue context, and persistent knowledge. It can learn distinct but related games, and can continue learning across dialogues for complex games. A novel sensing component supports adaptation to information-sharing differences across people. The single policy performs better than oracle policies customized to specific games and information behavior.
PaCo: Preconditions Attributed to Commonsense Knowledge
Authors: Filip Ilievski, Muhao Chen, Pedro Szekely Ehsan Qasemi
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Humans can seamlessly reason with circumstantial preconditions of commonsense knowledge. We understand that a glass is used for drinking water, unless the glass is broken or the water is toxic. Despite state-of-the-art (SOTA) language models' (LMs) impressive performance on inferring commonsense knowledge, it is unclear whether they understand the circumstantial preconditions. To address this gap, we propose a novel challenge of reasoning with circumstantial preconditions. We collect a dataset, called PaCo, consisting of 12.4 thousand preconditions of commonsense statements expressed in natural language. Based on this dataset, we create three canonical evaluation tasks and use them to examine the capability of existing LMs to understand situational preconditions. Our results reveal a 10-30% gap between machine and human performance on our tasks, which shows that reasoning with preconditions is an open challenge.
Improving Few-Shot Domain Transfer for Named Entity Disambiguation with Pattern Exploitation
Authors: Kfir Bar Philip Blair
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Named entity disambiguation (NED) is a critical subtask of entity linking, which seeks to connect knowledge base entities with textual mentions of those entities. Naturally, the performance of a model depends on the domain it was trained on; thus, reducing the amount of data required to train models is advantageous. In this work, we leverage recent research on pattern exploitation for NED and explore whether it can reduce the amount of data required for domain adaptation by reformulating the disambiguation task as a masked language modeling problem. Using ADAPET (Tam et al., 2021), which implements a new approach for few-shot learning using fine-tuned transformer-based language models, we produce an NED model which yields, without any sacrifice of in-domain accuracy, a 7% improvement in zero-shot cross-domain performance as evaluated on NEDMed, a new NED dataset of mental health news which we release with this work.
Capturing Topic Framing via Masked Language Modeling
Authors: Weicheng Ma, Soroush Vosoughi Xiaobo Guo
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Differential framing of issues can lead to divergent world views on important issues. This is especially true in domains where the information presented can reach a large audience, such as traditional and social media. Scalable and reliable measurement of such differential framing is an important first step in addressing them. In this work, based on the intuition that framing affects the tone and word choices in written language, we propose a framework for modeling the differential framing of issues through masked token prediction via large-scale fine-tuned language models (LMs). Specifically, we explore three key factors for our framework: 1) prompt generation methods for the masked token prediction; 2) methods for normalizing the output of fine-tuned LMs; 3) robustness to the choice of pre-trained LMs used for fine-tuning. Through experiments on a dataset of articles from traditional media outlets covering five diverse and politically polarized topics, we show that our framework can capture differential framing of these topics with high reliability.
WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation
Authors: Swabha Swayamdipta, Noah A. Smith, Yejin Choi Alisa Liu
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: A recurring challenge of crowdsourcing NLP datasets at scale is that human writers often rely on repetitive patterns when crafting examples, leading to a lack of linguistic diversity. We introduce a novel approach for dataset creation based on worker and AI collaboration, which brings together the generative strength of language models and the evaluative strength of humans. Starting with an existing dataset, MultiNLI for natural language inference (NLI), our approach uses dataset cartography to automatically identify examples that demonstrate challenging reasoning patterns, and instructs GPT-3 to compose new examples with similar patterns. Machine generated examples are then automatically filtered, and finally revised and labeled by human crowdworkers. The resulting dataset, WANLI, consists of 107,885 NLI examples and presents unique empirical strengths over existing NLI datasets. Remarkably, training a model on WANLI improves performance on eight out-of-domain test sets we consider, including by 11% on HANS and 9% on Adversarial NLI, compared to training on the 4x larger MultiNLI. Moreover, it continues to be more effective than MultiNLI augmented with other NLI datasets. Our results demonstrate the promise of leveraging natural language generation techniques and re-imagining the role of humans in the dataset creation process.
Sequentially Controlled Text Generation
Authors: Yao Ming, Xinyu Hua, Nanyun Peng Alexander Spangher
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: While GPT-2 generates sentences that are remarkably human-like, longer documents can ramble and do not follow human-like writing structure. We study the problem of imposing structure on long-range text. We propose a novel controlled text generation task, sequentially controlled text generation, and identify a dataset, NewsDiscourse as a starting point for this task. We develop a sequential controlled text generation pipeline with generation and editing. We test different degrees of structural awareness and show that, in general, more structural awareness results in higher control- accuracy, grammaticality, coherency and topicality, approaching human-level writing performance.
Revisiting the Roles of “Text” in Text Games
Authors: Shunyu Yao, Chuang Gan, Josh Tenenbaum, Mo Yu Yi Gu
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Text games present opportunities for natural language understanding (NLU) methods to tackle reinforcement learning (RL) challenges. However, recent work has questioned the necessity of NLU by showing random text hashes could perform decently. In this paper, we pursue a fine-grained investigation into the roles of text in the face of different RL challenges, and reconcile that semantic and non-semantic language representations could be complementary rather than contrasting. Concretely, we propose a simple scheme to extract relevant contextual information into an approximate state hash as extra input for an RNN-based text agent. Such a lightweight plug-in achieves competitive performance with state-of-the-art text agents using advanced NLU techniques such as knowledge graph and passage retrieval, suggesting non-NLU methods might suffice to tackle the challenge of partial observability. However, if we remove RNN encoders and use approximate or even ground-truth state hash alone, the model performs miserably, which confirms the importance of semantic function approximation to tackle the challenge of combinatorially large observation and action spaces. Our findings and analysis provide new insights for designing better text game task setups and agents.
FPT: Improving Prompt Tuning Efficiency via Progressive Training
Authors: Yujia Qin, Huadong Wang, Yichun Yin, Maosong Sun, Zhiyuan Liu, Qun Liu Yufei Huang
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Recently, prompt tuning (PT) has gained increasing attention as a parameter-efficient way of tuning pre-trained language models (PLMs). Despite extensively reducing the number of tunable parameters and achieving satisfying performance, PT is training-inefficient due to its slow convergence. To improve PT`s training efficiency, we first make some novel observations about the prompt transferability of “partial PLMs”, which are defined by compressing a PLM in depth or width. We observe that the soft prompts learned by different partial PLMs of various sizes are similar in the parameter space, implying that these soft prompts could potentially be transferred among partial PLMs. Inspired by these observations, we propose Fast Prompt Tuning (FPT), which starts by conducting PT using a small-scale partial PLM, and then progressively expands its depth and width until the full-model size. After each expansion, we recycle the previously learned soft prompts as initialization for the enlarged partial PLM and then proceed PT. We demonstrate the feasibility of FPT on 5 tasks and show that FPT could save over 30% training computations while achieving comparable performance. The codes are publicly available at https://github.com/thunlp/FastPromptTuning.
Prompt-learning for Fine-grained Entity Typing
Authors: Yulin Chen, Xu Han, Guangwei Xu, Xiaobin Wang, Pengjun Xie, Haitao Zheng, Zhiyuan Liu, Juanzi Li, Hong-Gee Kim Ning Ding
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: As an effective approach to adapting pre-trained language models (PLMs) for specific tasks, prompt-learning has recently attracted much attention from researchers. By using cloze-style language prompts to stimulate the versatile knowledge of PLMs, prompt-learning can achieve promising results on a series of NLP tasks, such as natural language inference, sentiment classification, and knowledge probing. In this work, we investigate the application of prompt-learning on fine-grained entity typing in fully supervised, few-shot, and zero-shot scenarios. We first develop a simple and effective prompt-learning pipeline by constructing entity-oriented verbalizers and templates and conducting masked language modeling. Further, to tackle the zero-shot regime, we propose a self-supervised strategy that carries out distribution-level optimization in prompt-learning to automatically summarize the information of entity types. Extensive experiments on four fine-grained entity typing benchmarks under fully supervised, few-shot, and zero-shot settings show the effectiveness of the prompt-learning paradigm and further make a powerful alternative to vanilla fine-tuning.
TransLIST: A Transformer-Based Linguistically Informed Sanskrit Tokenizer
Authors: Rathin Singha, Narein Rao, Suvendu Samanta, Laxmidhar Behera, Pawan Goyal Jivnesh Sandhan
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Sanskrit Word Segmentation (SWS) is essential in making digitized texts available and in deploying downstream tasks. It is, however, non-trivial because of the sandhi phenomenon that modifies the characters at the word boundaries, and needs special treatment. Existing lexicon driven approaches for SWS make use of Sanskrit Heritage Reader, a lexicon-driven shallow parser, to generate the complete candidate solution space, over which various methods are applied to produce the most valid solution. However, these approaches fail while encountering out-of-vocabulary tokens. On the other hand, purely engineering methods for SWS have made use of recent advances in deep learning, but cannot make use of the latent word information on availability. To mitigate the shortcomings of both families of approaches, we propose Transformer based Linguistically Informed Sanskrit Tokenizer (TransLIST) consisting of (1) a module that encodes the character input along with latent-word information, which takes into account the sandhi phenomenon specific to SWS and is apt to work with partial or no candidate solutions, (2) a novel soft-masked attention to prioritize potential candidate words and (3) a novel path ranking algorithm to rectify the corrupted predictions. Experiments on the benchmark datasets for SWS show that TransLIST outperforms the current state-of-the-art system by an average 7.2 points absolute gain in terms of perfect match (PM) metric.
Fair NLP Models with Differentially Private Text Encoders
Authors: Pascal Denis, Mikaela Keller, Aurélien Bellet Gaurav Maheshwari
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Encoded text representations often capture sensitive attributes about individuals (e.g., race or gender), which raise privacy concerns and can make downstream models unfair to certain groups. In this work, we propose FEDERATE, an approach that combines ideas from differential privacy and adversarial training to learn private text representations which also induces fairer models. We empirically evaluate the trade-off between the privacy of the representations and the fairness and accuracy of the downstream model on four NLP datasets. Our results show that FEDERATE consistently improves upon previous methods, and thus suggest that privacy and fairness can positively reinforce each other.
Modeling Context With Linear Attention for Scalable Document-Level Translation
Authors: Hao Peng, Nikolaos Pappas, Noah A. Smith Zhaofeng Wu
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Document-level machine translation leverages inter-sentence dependencies to produce more coherent and consistent translations. However, these models, predominantly based on transformers, are difficult to scale to long documents as their attention layers have quadratic complexity in the sequence length. Recent efforts on efficient attention improve scalability, but their effect on document translation remains unexplored. In this work, we investigate the efficacy of a recent linear attention model by Peng et al. (2021) on document translation and augment it with a sentential gate to promote a recency inductive bias. We evaluate the model on IWSLT 2015 and OpenSubtitles 2018 against the transformer, demonstrating substantially increased decoding speed on long sequences with similar or better BLEU scores. We show that sentential gating further improves translation quality on IWSLT.
What do Large Language Models Learn beyond Language?
Authors: Shashank Srivastava Avinash Madasu
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Large language models (LMs) have rapidly become a mainstay in Natural Language Processing. These models are known to acquire rich linguistic knowledge from training on large amounts of text. In this paper, we investigate if pre-training on text also confers these models with helpful \textquoteleftinductive biases' for non-linguistic reasoning. On a set of 19 diverse non-linguistic tasks involving quantitative computations, recognizing regular expressions and reasoning over strings. We find that pretrained models significantly outperform comparable non-pretrained neural models. This remains true also in experiments with training non-pretrained models with fewer parameters to account for model regularization effects. We further explore the effect of text domain on LMs by pretraining models from text from different domains and provenances. Our experiments surprisingly reveal that the positive effects of pre-training persist even when pretraining on multi-lingual text or computer code, and even for text generated from synthetic languages. Our findings suggest a hithertho unexplored deep connection between pre-training and inductive learning abilities of language models
CONSISTENT: Open-Ended Question Generation From News Articles
Authors: Justin Lewis, Smaranda Muresan Tuhin Chakrabarty
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Recent work on question generation has largely focused on factoid questions such as who, what,where, when about basic facts. Generating open-ended why, how, what, etc. questions thatrequire long-form answers have proven more difficult. To facilitate the generation of openended questions, we propose CONSISTENT, a new end-to-end system for generating openended questions that are answerable from and faithful to the input text. Using news articles asa trustworthy foundation for experimentation, we demonstrate our model`s strength over several baselines using both automatic and human based evaluations. We contribute an evaluationdataset of expert-generated open-ended questions. We discuss potential downstream applications for news media organizations.
Efficient (Soft) Q-Learning for Text Generation with Limited Good Data
Authors: Bowen Tan, Zhengzhong Liu, Eric Xing, Zhiting Hu Han Guo
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Maximum likelihood estimation (MLE) is the predominant algorithm for training text generation models. This paradigm relies on direct supervision examples, which is not applicable to many emerging applications, such as generating adversarial attacks or generating prompts to control language models. Reinforcement learning (RL) on the other hand offers a more flexible solution by allowing users to plug in arbitrary task metrics as reward. Yet previous RL algorithms for text generation, such as policy gradient (on-policy RL) and Q-learning (off-policy RL), are often notoriously inefficient or unstable to train due to the large sequence space and the sparse reward received only at the end of sequences. In this paper, we introduce a new RL formulation for text generation from the soft Q-learning (SQL) perspective. It enables us to draw from the latest RL advances, such as path consistency learning, to combine the best of on-/off-policy updates, and learn effectively from sparse reward. We apply the approach to a wide range of novel text generation tasks, including learning from noisy/negative examples, adversarial attacks, and prompt generation. Experiments show our approach consistently outperforms both task-specialized algorithms and the previous RL methods.
Lexi: Self-Supervised Learning of the UI Language
Authors: Shweti Mahajan, Kushal Arora, Chitta Baral, Oriana Riva Pratyay Banerjee
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Humans can learn to operate the user interface (UI) of an application by reading an instruction manual or how-to guide. Along with text, these resources include visual content such as UI screenshots and images of application icons referenced in the text. We explore how to leverage this data to learn generic visio-linguistic representations of UI screens and their components. These representations are useful in many real applications, such as accessibility, voice navigation, and task automation. Prior UI representation models rely on UI metadata (UI trees and accessibility labels), which is often missing, incompletely defined, or not accessible. We avoid such a dependency, and propose Lexi, a pre-trained vision and language model designed to handle the unique features of UI screens, including their text richness and context sensitivity. To train Lexi we curate the UICaption dataset consisting of 114k UI images paired with descriptions of their functionality. We evaluate Lexi on four tasks: UI action entailment, instruction-based UI image retrieval, grounding referring expressions, and UI entity recognition.
Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning
Authors: Siyan Li, Sarah Wiegreffe, Mark Riedl Xiangyu Peng
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Transformer-based language model approaches to automated story generation currently provide state-of-the-art results. However, they still suffer from plot incoherence when generatingnarratives over time, and critically lack basiccommonsense reasoning. Furthermore, existing methods generally focus only on single-character stories, or fail to track charactersat all. To improve the coherence of generated narratives and to expand the scope ofcharacter-centric narrative generation, we introduce Commonsense-inference Augmentedneural StoryTelling (CAST), a framework forintroducing commonsense reasoning into thegeneration process with the option to model theinteraction between multiple characters. Wefind that our CAST method produces significantly more coherent, on-topic, enjoyable andfluent stories than existing models in both thesingle-character and two-character settings inthree storytelling domains.
How to Stop an Avalanche? JoDeM: Joint Decision Making through Compare and Contrast for Dialog State Tracking
Authors: Wang Xin Haoming Wang
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Dialog state tracking (DST) is a core component in task-oriented dialog systems. Existing state-of-the-art DST model incorporates insight and intuition from the human experience into design of supplementary labels, which greatly assisted the training process of turn-by-turn DST model. Though the turn-by-turn scheme and supplementary labels enabled satisfactory performance on the task, most of the DST models of this fashion label or process the raw dialogue data on the premise that the last turn dialogue state is always correct, which is usually not the case. In this paper, we address the negative impact resulted from the premise above as the avalanche phenomenon. After that, we propose JoDeM, a state-of-the-art DST model which can tackle the Avalanche phenomenon with two mechanisms. First mechanism is a jointly decision making method to extract key information from the dialogue. Second mechanism is a compare and contrast dialogue update technique to prevent error accumulation. Example study and graph analysis are presented to support our claim about the harmfulness of avalanche phenomenon. We also conduct quantitative and qualitative experiments on the high quality MultiWOZ2.3 corpus dataset to demonstrate that the proposed model not only outperforms the existing state-of-the-art methods, but also proves the validity of solving avalanche degradation problem.
Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding
Authors: Yongjing Yin, Yufan Jiang, Shuangzhi Wu, Yunbo Cao Jiali Zeng
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Contrastive learning has become a new paradigm for unsupervised sentence embeddings.Previous studies focus on instance-wise contrastive learning, attempting to construct positive pairs with textual data augmentation. In this paper, we propose a novel Contrastive learning method with Prompt-derived Virtual semantic Prototypes (ConPVP). Specifically, with the help of prompts, we construct virtual semantic prototypes to each instance, and derive negative prototypes by using the negative form of the prompts.Using a prototypical contrastive loss, we enforce the anchor sentence embedding to be close to its corresponding semantic prototypes, and far apart from the negative prototypes as well as the prototypes of other sentences.Extensive experimental results on semantic textual similarity, transfer, and clustering tasks demonstrate the effectiveness of our proposed model compared to strong baselines.Code is available at https://github.com/lemon0830/promptCSE.
Weight Perturbation as Defense against Adversarial Word Substitutions
Authors: Linyang Li, Jiping Zhang, Xiaoqing Zheng, Kai-Wei Chang, Cho-Jui Hsieh, Xuanjing Huang Jianhan Xu
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: The existence and pervasiveness of textual adversarial examples have raised serious concerns to security-critical applications. Many methods have been developed to defend against adversarial attacks for neural natural language processing (NLP) models.Adversarial training is one of the most successful defense methods by adding some random or intentional perturbations to the original input texts and making the models robust to the perturbed examples.In this study, we explore the feasibility of improving the adversarial robustness of NLP models by performing perturbations in the parameter space rather than the input feature space.The weight perturbation helps to find a better solution (i.e., the values of weights) that minimizes the adversarial loss among other feasible solutions.We found that the weight perturbation can significantly improve the robustness of NLP models when it is combined with the perturbation in the input embedding space, yielding the highest accuracy on both clean and adversarial examples across different datasets.
CORT: A New Baseline for Comparative Opinion Classification by Dual Prompts
Authors: Hengran Zhang, Aixin Sun, Xuying Meng Yequan Wang
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Comparative opinion is a common linguistic phenomenon. The opinion is expressed by comparing multiple targets on a shared aspect, e.g., “camera A is better than camera B in picture quality”. Among the various subtasks in opinion mining, comparative opinion classification is relatively less studied. Current solutions use rules or classifiers to identify opinions, i.e., better, worse, or same, through feature engineering. Because the features are directly derived from the input sentence, these solutions are sensitive to the order of the targets mentioned in the sentence. For example, “camera A is better than camera B” means the same as “camera B is worse than camera A”; but the features of these two sentences are completely different. In this paper, we approach comparative opinion classification through prompt learning, taking the advantage of embedded knowledge in pre-trained language model. We design a twin framework with dual prompts, named CORT. This extremely simple model delivers state-of-the-art and robust performance on all benchmark datasets for comparative opinion classification. We believe CORT well serves as a new baseline for comparative opinion classification.
APEACH: Attacking Pejorative Expressions with Analysis on Crowd-Generated Hate Speech Evaluation Datasets
Authors: Wonjun Jang, Won Ik Cho Kichang Yang
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: In hate speech detection, developing training and evaluation datasets across various domains is the critical issue. Whereas, major approaches crawl social media texts and hire crowd-workers to annotate the data. Following this convention often restricts the scope of pejorative expressions to a single domain lacking generalization. Sometimes domain overlap between training corpus and evaluation set overestimate the prediction performance when pretraining language models on low-data language. To alleviate these problems in Korean, we propose APEACH that asks unspecified users to generate hate speech examples followed by minimal post-labeling. We find that APEACH can collect useful datasets that are less sensitive to the lexical overlaps between the pretraining corpus and the evaluation set, thereby properly measuring the model performance.