Reviewerly/exHarmony huggingface dataset:
Clip-Tuning: Towards Derivative-free Prompt Learning with a Mixture of Rewards

Derivative-free prompt learning has emerged as a lightweight alternative to prompt tuning, which only requires model inference to optimize the prompts. However, existing work did not take full advantage of the over-parameterized characteristics of large pre-trained language models (PLMs). In this paper, we propose Clip-Tuning, a simple yet effective method that adopts diverse frozen "thinned" networks of PLMs to obtain *a mixture of rewards* and thus advance the derivative-free prompt learning. The thinned networks consist of all the hidden units that survive a stationary dropout strategy, whose inference predictions reflect an ensemble of partial views over prompted training samples. Our method outperforms previous gradient-free prompt learning methods and achieves parity with gradient-based counterparts on seven language understanding benchmarks under few-shot settings.
Empathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge Selection

Empathy, which is widely used in psychological counselling, is a key trait of everyday human conversations.Equipped with commonsense knowledge, current approaches to empathetic response generation focus on capturing implicit emotion within dialogue context, where the emotions are treated as a static variable throughout the conversations.However, emotions change dynamically between utterances, which makes previous works difficult to perceive the emotion flow and predict the correct emotion of the target response, leading to inappropriate response.Furthermore, simply importing commonsense knowledge without harmonization may trigger the conflicts between knowledge and emotion, which confuse the model to choose incorrect information to guide the generation process.To address the above problems, we propose a Serial Encoding and Emotion-Knowledge interaction (SEEK) method for empathetic dialogue generation.We use a fine-grained encoding strategy which is more sensitive to the emotion dynamics (emotion flow) in the conversations to predict the emotion-intent characteristic of response.Besides, we design a novel framework to model the interaction between knowledge and emotion to generate more sensible response.Extensive experiments on EMPATHETICDIALOGUES demonstrate that SEEK outperforms the strong baselines in both automatic and manual evaluations.
CDialog: A Multi-turn Covid-19 Conversation Dataset for Entity-Aware Dialog Generation

The development of conversational agents to interact with patients and deliver clinical advice has attracted the interest of many researchers, particularly in light of the COVID-19 pandemic.The training of an end-to-end neural based dialog system, on the other hand, is hampered by a lack of multi-turn medical dialog corpus.We make the very first attempt to release a highquality multi-turn Medical Dialog dataset relating to Covid-19 disease named CDialog, with over 1K conversations collected from the online medical counselling websites.We annotate each utterance of the conversation with seven different categories of medical entities, including diseases, symptoms, medical tests, medical history, remedies, medications and other aspects as additional labels.Finally, we propose a novel neural medical dialog system based on the CDialog dataset to advance future research on developing automated medical dialog systems.We use pre-trained language models for dialogue generation, incorporating annotated medical entities, to generate a virtual doctor's response that addresses the patient's query.Experimental results show that the proposed dialog models perform comparably better when supplemented with entity information and hence can improve the response quality.
Revisiting the Roles of “Text” in Text Games

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.* Equal contribution.Observation: Kitchen.You are in the kitchen of the white house… There is a brass lantern (battery-powered) here… Look: <Same as Observation> Inventory: A glass bottle containing water.Observation: You have moved into a dark place.The trap door crashes shut, and you hear someone barring it.It is pitch black.You are likely to be eaten by a grue.Look: It is pitch black.You are likely to be eaten by a grue.Inventory: A glass bottle containing water.A brown sack.Observation: Living Room.Above the trophy case hangs an elvish sword of great antiquity.Look: Living Room.There is a doorway to the east, …,
ER-Test: Evaluating Explanation Regularization Methods for Language Models

By explaining how humans would solve a given task, human rationales can provide strong learning signal for neural language models (NLMs).Explanation regularization (ER) aims to improve NLM generalization by pushing the NLM's machine rationales (Which input tokens did the NLM focus on?) to align with human rationales (Which input tokens would humans focus on?).Though prior works primarily study ER via in-distribution (ID) evaluation, out-ofdistribution (OOD) generalization is often more critical in real-world scenarios, yet ER's effect on OOD generalization has been underexplored.In this paper, we introduce ER-TEST, a framework for evaluating ER models' OOD generalization along three dimensions: unseen datasets, contrast set tests, and functional tests.Using ER-TEST, we comprehensively analyze how ER models' OOD generalization varies with the rationale alignment criterion (loss function), human rationale type (instance-level vs. task-level), number and choice of rationaleannotated instances, and time budget for rationale annotation.Across two tasks and six datasets, we show that ER has little impact on ID performance but yields large OOD performance gains, with the best ER criterion being task-dependent.Also, ER can improve OOD performance even with task-level or few human rationales.Finally, we find that rationale annotation is more time-efficient than label annotation for improving OOD performance.Our results with ER-TEST help demonstrate ER's utility and establish best practices for using ER effectively.
A Generative User Simulator with GPT-based Architecture and Goal State Tracking for Reinforced Multi-Domain Dialog Systems

Building user simulators (USs) for reinforcement learning (RL) of task-oriented dialog systems (DSs) has gained more and more attention, which, however, still faces several fundamental challenges.First, it is unclear whether we can leverage pretrained language models to design, for example, GPT-2 based USs, to catch up and interact with the recently advanced GPT-2 based DSs.Second, an important ingredient in a US is that the user goal can be effectively incorporated and tracked; but how to flexibly integrate goal state tracking and develop an end-to-end trainable US for multi-domains has remained to be a challenge.In this work, we propose a generative user simulator (GUS) with GPT-2 based architecture and goal state tracking towards addressing the above two challenges.Extensive experiments are conducted on MultiWOZ2.1.Different DSs are trained via RL with GUS, the classic agenda-based user simulator (ABUS) and other ablation simulators respectively, and are compared for crossmodel evaluation, corpus-based evaluation and human evaluation.The GUS achieves superior results in all three evaluation tasks.
NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data Augmentation

While counterfactual data augmentation offers a promising step towards robust generalization in natural language processing, producing a set of counterfactuals that offer valuable inductive bias for models remains a challenge.Most existing approaches for producing counterfactuals, manual or automated, rely on small perturbations via minimal edits, resulting in simplistic changes.We introduce NeuroCounterfactuals, designed as loose counterfactuals, allowing for larger edits which result in naturalistic generations containing linguistic diversity, while still bearing similarity to the original document.Our novel generative approach bridges the benefits of constrained decoding, with those of language model adaptation for sentiment steering.Training data augmentation with our generations results in both in-domain and out-of-domain improvements for sentiment classification, outperforming even manually curated counterfactuals, under select settings.We further present detailed analyses to show the advantages of NeuroCounterfactuals over approaches involving simple, minimal edits.
Saving Dense Retriever from Shortcut Dependency in Conversational Search

Conversational search (CS) needs a holistic understanding of conversational inputs to retrieve relevant passages.In this paper, we demonstrate the existence of a retrieval shortcut in CS, which causes models to retrieve passages solely relying on partial history while disregarding the latest question.With in-depth analysis, we first show that naively trained dense retrievers heavily exploit the shortcut and hence perform poorly when asked to answer historyindependent questions.To build more robust models against shortcut dependency, we explore various hard negative mining strategies.Experimental results show that training with the model-based hard negatives (Xiong et al., 2020) effectively mitigates the dependency on the shortcut, significantly improving dense retrievers on recent CS benchmarks.In particular, our retriever outperforms the previous state-of-the-art model by 11.0 in Recall@10 on QReCC (Anantha et al., 2021). 1
Adapting a Language Model While Preserving its General Knowledge

Domain-adaptive pre-training (or DA-training for short), also known as post-training, aims to train a pre-trained general-purpose language model (LM) using an unlabeled corpus of a particular domain to adapt the LM so that endtasks in the domain can give improved performances.However, existing DA-training methods are in some sense blind as they do not explicitly identify what knowledge in the LM should be preserved and what should be changed by the domain corpus.This paper shows that the existing methods are suboptimal and proposes a novel method to perform a more informed adaptation of the knowledge in the LM by (1) soft-masking the attention heads based on their importance to best preserve the general knowledge in the LM and (2) contrasting the representations of the general and the full (both general and domain knowledge) to learn an integrated representation with both general and domain-specific knowledge.Experimental results will demonstrate the effectiveness of the proposed approach.
Efficient Zero-shot Event Extraction with Context-Definition Alignment

Event extraction (EE) is the task of identifying interested event mentions from text.Conventional efforts mainly focus on the supervised setting.However, these supervised models cannot generalize to event types out of the pre-defined ontology.To fill this gap, many efforts have been devoted to the zero-shot EE problem.This paper follows the trend of modeling event-type semantics but moves one step further.We argue that using the static embedding of the event type name might not be enough because a single word could be ambiguous, and we need a sentence to define the type semantics accurately.To model the definition semantics, we use two separate transformer models to project the contextualized event mentions and corresponding definitions into the same embedding space and then minimize their embedding distance via contrastive learning.On top of that, we also propose a warming phase to help the model learn the minor difference between similar definitions.We name our approach Zero-shot Event extraction with Definition (ZED).Experiments on the MAVEN dataset show that our model significantly outperforms all previous zero-shot EE methods with fast inference speed due to the disjoint design.Further experiments also show that ZED can be easily applied to the few-shot setting when the annotation is available and consistently outperforms baseline supervised methods.
Partially-Random Initialization: A Smoking Gun for Binarization Hypothesis of BERT

In the past few years, pre-trained BERT has become one of the most popular deeplearning language models due to their remarkable performance in natural language processing (NLP) tasks.However, the superior performance of BERT comes at the cost of high computational and memory complexity, hindering its envisioned widespread deployment in edge devices with limited computing resources.Binarization can alleviate these limitations by reducing storage requirements and improving computing performance.However, obtaining a comparable accuracy performance for binary BERT w.r.t.its full-precision counterpart is still a difficult task.We observe that direct binarization of pre-trained BERT provides a poor initialization during the fine-tuning phase, making the model incapable of achieving a decent accuracy on downstream tasks.Based on this observation, we put forward the following hypothesis: partially randomlyinitialized BERT with binary weights and activations can reach to a decent accuracy performance by distilling knowledge from the its full-precision counterpart.We show that BERT with pre-trained embedding layer and randomly-initialized encoder is a smoking gun for this hypothesis.We identify the smoking gun through a series of experiments and show that it yields a new set of state-of-the-art results on the GLUE and SQuAD benchmarks.
ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples

Reasoning over tabular data requires both table structure understanding and a broad set of table reasoning skills.Current models with tablespecific architectures and pre-training methods perform well on understanding table structures, but they still struggle with tasks that require various table reasoning skills.In this work, we develop REASTAP to show that high-level table reasoning skills can be injected into models during pre-training without a complex tablespecific architecture design.We define 7 table reasoning skills, such as numerical operation, temporal comparison, and conjunction.Each reasoning skill is associated with one example generator, which synthesizes questions over semi-structured tables according to the sampled templates.We model the table pre-training task as a sequence generation task and pretrain REASTAP to generate precise answers to the synthetic examples.REASTAP is evaluated on four benchmarks covering three downstream tasks including: 1) WIKISQL-WEAK and WIKITQ for Table Question Answering; 2) TABFACT for Table Fact Verification; and 3) LOGICNLG for Faithful Table-to-Text Generation.Experimental results demonstrate that REASTAP achieves new state-of-the-art performance on all benchmarks and delivers a significant improvement on low-resource setting.
Towards Summary Candidates Fusion

Sequence-to-sequence deep neural models fine-tuned for abstractive summarization can achieve great performance on datasets with enough human annotations.Yet, it has been shown that they have not reached their full potential, with a wide gap between the top beam search output and the oracle beam.Recently, re-ranking methods have been proposed, to learn to select a better summary candidate.However, such methods are limited by the summary quality aspects captured by the first-stage candidates.To bypass this limitation, we propose a new paradigm in secondstage abstractive summarization called Sum-maFusion that fuses several summary candiates to produce a novel abstractive secondstage summary.Our method works well on several summarization datasets, improving both the ROUGE scores and qualitative properties of fused summaries.It is especially good when the candidates to fuse are worse, such as in the few-shot setup where we set a new state-of-the-art.We will make our code and checkpoints available at https: //github.com/ntunlp/SummaFusion/. Model
Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts

Crises such as the COVID-19 pandemic continuously threaten our world and emotionally affect billions of people worldwide in distinct ways.Understanding the triggers leading to people's emotions is of crucial importance.Social media posts can be a good source of such analysis, yet these texts tend to be charged with multiple emotions, with triggers scattering across multiple sentences.This paper takes a novel angle, namely, emotion detection and trigger summarization, aiming to both detect perceived emotions in text, and summarize events and their appraisals that trigger each emotion.To support this goal, we introduce COVIDET (Emotions and their Triggers during Covid-19), a dataset of ~1, 900 English Reddit posts related to COVID-19, which contains manual annotations of perceived emotions and abstractive summaries of their triggers described in the post.We develop strong baselines to jointly detect emotions and summarize emotion triggers.Our analyses show that COVIDET presents new challenges in emotion-specific summarization, as well as multi-emotion detection in long social media posts.* Hongli Zhan and Tiberiu Sosea contributed equally. Reddit Post1: My sibling is 19 and she constantly goes places with her friends and to there houses and its honestly stressing me out.2: Our grandfather lives with us and he has dementia along with other health issues and my mom has diabetes and heart problems and I have autoimmune diseases & chronic health issues.3: She also has asthma.4: Its stressing me out because despite this she seems to not care about how badly it would affect all of us if we were to get the virus.5: And sadly I feel like its not much I can do she literally doesn't respect my mom and though I'm older she doesn't respect me either.6: Its so frustrating. Emotions and Abstractive Summaries of TriggersEmotion: anger Abstractive Summary of Trigger: My sister having absolutely no regard for any of our family's health coupled with the fact that I can't do anything about it is so aggravating to me.Emotion: fear Abstractive Summary of Trigger: My sibling, who, in spite of our family's myriad of issues that all make us high-risk people, continuously goes out and about, which makes her likely to get infected.I am scared for all of us right now.
A Robust Bias Mitigation Procedure Based on the Stereotype Content Model

The Stereotype Content model (SCM) states that we tend to perceive minority groups as cold, incompetent or both.In this paper we adapt existing work to demonstrate that the Stereotype Content model holds for contextualised word embeddings, then use these results to evaluate a fine-tuning process designed to drive a language model away from stereotyped portrayals of minority groups.We find the SCM terms are better able to capture bias than demographic agnostic terms related to pleasantness.Further, we were able to reduce the presence of stereotypes in the model through a simple fine-tuning procedure that required minimal human and computer resources, without harming downstream performance.We present this work as a prototype of a debiasing procedure that aims to remove the need for a priori knowledge of the specifics of bias in the model.
The Undesirable Dependence on Frequency of Gender Bias Metrics Based on Word Embeddings

Numerous works use word embedding-based metrics to quantify societal biases and stereotypes in texts.Recent studies have found that word embeddings can capture semantic similarity but may be affected by word frequency.In this work we study the effect of frequency when measuring female vs. male gender bias with word embedding-based bias quantification methods.We find that Skip-gram with negative sampling and GloVe tend to detect male bias in high frequency words, while GloVe tends to return female bias in low frequency words.We show these behaviors still exist when words are randomly shuffled.This proves that the frequency-based effect observed in unshuffled corpora stems from properties of the metric rather than from word associations.The effect is spurious and problematic since bias metrics should depend exclusively on word cooccurrences and not individual word frequencies.Finally, we compare these results with the ones obtained with an alternative metric based on Pointwise Mutual Information.We find that this metric does not show a clear dependence on frequency, even though it is slightly skewed towards male bias across all frequencies.
WikiOmnia: filtration and evaluation of the generated QA corpus on the whole Russian Wikipedia

The General QA field has been developing the methodology referencing the Stanford Question answering dataset (SQuAD) as the significant benchmark.Compiling factual questions datasets requires manual annotations, limiting the training data's potential size.We present the WikiOmnia dataset, a new publicly available set of QA pairs and corresponding Russian Wikipedia article summary sections, composed with a fully automated generation and filtration pipeline.To ensure high quality of generated QA pairs, diverse manual and automated evaluation techniques were applied.The WikiOmnia pipeline is available opensource and is also tested for creating SQuADformatted QA on other domains, like news texts, fiction, and social media.The resulting dataset includes two parts: raw data on the whole Russian Wikipedia (7,930,873 QA pairs with paragraphs for ruGPT-3 XL and 7,991,040 QA pairs with paragraphs for ruT5-large) and cleaned data with strict automatic verification (over 160,000 QA pairs with paragraphs for ruGPT-3 XL and over 3,400,000 QA pairs with paragraphs for ruT5-large).
Dim-Krum: Backdoor-Resistant Federated Learning for NLP with Dimension-wise Krum-Based Aggregation

Despite the potential of federated learning, it is known to be vulnerable to backdoor attacks.Many robust federated aggregation methods are proposed to reduce the potential backdoor risk.However, they are mainly validated in the CV field.In this paper, we find that NLP backdoors are hard to defend against than CV, and we provide a theoretical analysis that the malicious update detection error probabilities are determined by the relative backdoor strengths.NLP attacks tend to have small relative backdoor strengths, which may result in the failure of robust federated aggregation methods for NLP attacks.Inspired by the theoretical results, we can choose some dimensions with higher backdoor strengths to settle this issue.We propose a novel federated aggregation algorithm, Dim-Krum, for NLP tasks, and experimental results validate its effectiveness.