K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
Authors: Ruize Wang
Duyu Tang
Nan Duan
zhongyu wei
Xuanjing Huang
Jianshu Ji
Guihong Cao
Daxin Jiang
Ming Zhou
Venue:  (2021)

Abstract: We study the problem of injecting knowledge into large pre-trained models like BERT and RoBERTa. Existing methods typically update the original parameters of pre-trained models when injecting knowledge. However, when multiple kinds of knowledge are injected, they may suffer from catastrophic forgetting.  To address this, we propose K-Adapter, which remains the original parameters of the pre-trained model fixed and supports continual knowledge infusion. Taking RoBERTa as the pre-trained model, K-Adapter has a neural adapter for each kind of infused knowledge, like a plug-in connected to RoBERTa. There is no information flow between different adapters, thus different adapters are efficiently trained in a distributed way. We inject two kinds of knowledge, including factual knowledge obtained from automatically aligned text-triplets on Wikipedia and Wikidata, and linguistic knowledge obtained from dependency parsing. Results on three knowledge-driven tasks (total six datasets) including relation classification, entity typing and question answering demonstrate that each adapter improves the performance, and the combination of both adapters brings further improvements. Probing experiments further indicate that K-Adapter captures richer factual and commonsense knowledge than RoBERTa.

Under review as a conference paper at ICLR 2021
A-DPPTEER: INFUSING KNOWLEDGE INTO PRE- TRAINED MODELS WITH ADAPTERS
Anonymous authors Paper under double-blind review
ABSTRACT
We study the problem of injecting knowledge into large pre-trained models like BERT and RoBERTa. Existing methods typically update the original parame- ters of pre-trained models when injecting knowledge. However, when multiple kinds of knowledge are injected, they may suffer from catastrophic forgetting. To address this, we propose K-ADARTER, which retains the original parameters of the pre-trained model fixed and supports continual knowledge infusion. Taking RoBERTa as the pre-trained model, K-ADAPTER has neural adapter for each kind of infused knowledge, like a plug-in connected to RoBERTa. There is no information flow between different adapters, thus different adapters are efficiently trained in distributed way. We inject two kinds of knowledge, including fac- tual knowledge obtained from automatically aligned text-triplets on Wikipedia and Wikidata, and linguistic knowledge obtained from dependency parsing. Results three knowledge-driven tasks (total six datasets) including relation classification, entity typing and question answering demonstrate that each adapter improves the performance, and the combination of both adapters brings further improvements. Probing experiments further indicate that K-ADAPTER captures richer factual and commonsense knowledge than RoBERTa.
INTRODUCTION
Language representation models, which are pre-trained on large-scale text corpus through unsu- pervised objectives like (masked) language modeling, such as BERT (Devlin et al. 2019), GPT (Radford et al. 2018 2019), XLNet (Yang et al. 2019p, RoBERTa (Lu et al. 2019) and T5 (Rafeel al. 2019), have established state-of-the-ar performances various NLP downstream tasks.
Despite the huge success of these pre-trained models in empirical studies, recent studies suggest that models learned in such an unsupervised manner struggle capture rich knowledge. For example, Poerner et al. (209)) suggest that although language models do well in reasoning about the surface form of entity names, they fail in capturing rich factual knowledge. Kassner & Schütze (2019) observe that BERT mostly did not learn the meaning of negation (e.g. "not"). These observations motivate study the injection of knowledge into pre-trained models like BERT and RoBERTa.
Recently, some efforts have been made to exploit injecting knowledge into pre-trained language models Zhang et al., 22019 Lauscher et al.] 2019 Levine al., 2019 Peters al. 2019 He al. 2019 Xiong et al. (020). Most previous works (as shown in Table augment the standard language modeling objective with knovledge-driven objectives and update model parameters in multi-task learning manner. Although these methods, with updated pre-trained models, obtain better perfor- mance on downstream tasks, they fail continual learning (Kirkpatrick et al. 2017). Model param- eters need to be retrained when new kinds of knowledge are injected, which may result in the catas- trophic forgetting of previously injected knowledge. Meanwhile, the resulting pre-trained models produce entangled representationss which makes it hard to investigate the effect of each knowledge when multiple kinds of knowledge are injected.
In this paper, propose K-ADAPTER, flexible and simple approach that infuses knowledge into large pre-trained models. K-ADAPTER has attractive properties including supporting continual knowledge infusion and producing disentangled representations. It leaves the original representation of pre-trained model unchanged and exports different representations for different types of infused knowledge. This is achieved by the integration of compact neural models, dubbed adapters here.

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Table 1: Comparison between our approach (K (K-ADAPTER) and previous works on injecting knowl- edge into BERT.
Model
Knowledge Source
Objective
BERT fixed
Continual knowl- edge infusion?
in training?
ERNIE Zhang ct al.
Wikipedia, WikiData
cntity linking
N
N
2019)
LIBERT
Lauscher
WordNet
synonym word prediction, hyponym-hhpermym prediction word-supersense prediction
from scratch
2019
Sonse BEKT
Levine
WordNet
from scratch
sr al. 2019
KnowBEKT
Peters
Wordnet, Wikipedia, CrossWikis
entity linking hypernym link- ing replaced entity detection
N
N
cr al. 2019
WKLM (Xiong et al.
WikiPedia, WikiData
N
N
BERT-MK le al.
Unified Medical Lan-
discritninate between real and fake facts
N
2019
guage System
K Adapter (this work)
Wikipedia, Wikidata,
predication prediction depen-
dependency parser
dency relution prediction
Adapters are knowledge-specifie models plugged outside of a pre-trained model, whose inputs are the output hidden-states of intermediate layers of the pre-trained model. We take RoBERTa (Liu et al. 2019) the base pre-trained model and integrate two types of knowledge, including factual knowledge obtained by aligned Wikipedia text to Wikidata triplets, linguistic knowledge obtained by applying off-the-shell dependency parser web texts. In the pre-training phase, train two adapters independently on relation classification task and dependency relation prediction task re- spectively, while keeping the original parameters of RoBERTa frozen. Since adapters have much less trainable parameters compared with RoBERTa, the training process is memory efficient.
We conduct extensive experiments six benchmark datasets across three knowledge-driven tasks, i.c., relation classification, entity typing and question answering. Experiments show that K- ADAPTER consistently performs better than RoBERTa, and achieves state-of-the-art performance on five datasets, and comparable performance compared with CosmosQA SOTA. Probing experiments on LAMA (Poerner et al. 2019) and LAMA-UHN (Petroni et al., 2019), further demonstrates that K-ADAPTER captures richer factual and commonsense knowledge than RoBERTa.
The contributions this paper are summarized as follows:
We propose K-ADAPTER, flexible approach that supports continual knowledge infusion into large pre-trained models (e.g. RoBERTa in this work).
We infuse factual knowledge and linguistic knowledge, and show that adapters for both kinds of knowledge work well on downstream tasks.
K-ADAPTER achieves superior performance by fine-tuning parameters on three down- stream tasks, and captures richer factual and commonsense knowledge than RoBERTa on probing experiments.
RELATED WORK
Our work relates the area of injecting knowledge into pre-trained models. As stated in Table !| previous works mainly differ from the knowledge sources and the objective used for training.
ERNIE (Zhag et al. 2019) injects a knowledge graph into BERT. They align entities from Wikipedia sentences to fact triples in WikiData, and discard sentences with less than three enti- ties. In the training process, the input includes sentences and linked facts, and the knowledge-awwe aware learning objective is to predict the correct token-entity alignment. Entity embeddings are trained on fact triples from WikiData via TransE (Bordes et al. 2013). LIBERT (Lauscher et al. 2019) injects pairs of words with synonym and ayponnymhypeenyye relations in WordNet. The model takes pair of words separated by special token as the input, and is optimized by a binary classification prob- lem, which predicts whether the input holds particular relation not. SenseBERT (Levine al. 2019) considers word- surr-supeeee knowledge. inject knowledge by predicting the supersense of the masked word in the input, where the candidates nouns and verbs and the ground truth comes
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Imput ImputTokess Tokins
Language Model
Pro frain lasky
TRM
TRMM
Pre-train
lasky
James
dheass Knowledge
(6) Multi-Tssk Masi--Tskk Learning
Inpul Takana
Language Model
TRM
TRM
TRM
TRMM
Danman
Pro-train
DC
task:
Adapler
Pre-train ta5kg
Factual Knowledge
Adapteta
KIA
KIA
KIAK
(o) K-Adapter
Figure 1: (a) Pre-traines language models inject multiple kinds of knowledge with multi-task learn- ing. Model parameters need to be retrained when injecting new kinds of knowledge, which may result in the catastrophic forgetting (b) Our K-ADAPTER injects multiple kinds of knowledge by training adapters independently
different pre-train tasks, which supports continual knowledge infusion. When we inject new kinds of knowledge, the existing knowledge-specific adapters will be affected. KIA represents the adapter layer and TRM represents the transformer layer, both of which are shown in Figure 2
from WordNet. KnowBERT Peeters et al. 2019) incorporates knowledge bases into BERT using Knowledge attention and recontextualization where the knowledge comes from synset-synsel and lemma-lemma relationships in WordNet, and entity linking information in Wikipedia. entity link- ing supervision is available, the model is learned with an additional prowledge-wward log likeeiidddood or max-marga margin objective. WKLM (Xiong al. 2020) replaces entity mentions in the original doc- ument with names of other entities of the same type. The model is trained to distinguish the correct entity mention from randomly chosen ones. BERT-MK (He et al. 2019) integrates fact triples from knowledge graph. For each entity, it sample incoming and outcoming instances from the neighbors on the knowledge graph, and replaces head or tail entity to create negative instances. The model is learned to discriminate between real and fake facts.
As shown in Table D our model (K ADAPTER) differs from previous studies in three aspects. First, we consider both fact-related objec- tive (i.e. predicate/relation prediction) and linguistic-related objective (i.e. dependency re- lation prediction). Second, the original param- eter of BERT is clamped in the knowledge in- fusion process. Third, our approach supports continual learning, which means that the learn- ing of different adapters are not entangled. This flexibility enables us to efficiently inject differ- ent types of knowledge independently, and in- ject more types of knowledge without any loss on the previously injected knowledge.
Transformer Layer (TFM)
Adapter (KIA)
Add Norm
Up Projection Layer A
Feldd Fonward
Tranetormer
Laver
Add & Norm
Down Projection)
Layer
Mull-heed Altention
Figure 2: Structure of the adapter layer (left). The adapter layer consists of two projection layers and N=2 transformer layers, and skip-connection between two projection layers.
K-ADAPTER
As illustrated in Figure (a), most of the previous works enhance pre-trained language models by injecting knowledge and update model parameters through multi-task learning. Regardless of these different versions of knowledge-njjected methods with multi-task learning, common issues not fully studied are catastrophic forgetting of previous knowledge. To address this, we present injected into different
ADAPTER shown in Figure bb), where multiple kinds of knowledge compact neural models (i.c., adapters this paper) individually instead of directly injecting knowl- edge into pre-trained models. It keeps the original representation of pre-trained model fixed and
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supports continual knowledge infusion, i.e., injecting each kind of knowledge into the correspond- ing knowledge-speeet adapter and producing disentangled representation. Specifically, adapters are knowledge-specifff models (with few parameters) plugged outside of pre-trained model. The inputs of adapters are the output hidden-states of intermediate layers of the pre-trained model. Each adapter is pre-trained independently on different tasks for injecting discriminative knowledge while the original parameters of the pre-trained model are frozen. In this paper, we exploit RoBERTa (Lu al. 2019) as the pre-trained model, and mainly infuse factual knowledge and linguistic knowledge with two kinds of adapters, i.e., factual adapter and linguistic adapter which are pre-trained on the relation classification task and dependency relation prediction task respectively. In this section, we first describe the structure of our adapter, and then present the process of pre-training knowledge- specific adapters.
3.1 ADAPTER STRUCTURE
In this work, we present a different adapter structure as shown in Figure which is referred as the knowledge-specific adapter. In contrast to Houlsby et al. (2019) add adapter layers into each transformer layer, our adapter works as outside plug-ins. Each adapter model consists of K adapter layers that contain N transformer (Vaswani et al. 2017) layers and two projection layers. A skip- connection is applied across two projection layers. Specifically, for each adapter model, we plug adapter layers among different transformer layers of the pre-trained model. We concatenate the output hidden feature of the transformer layer in the pre-trained model and the output feature of the former adapter layer, as the input feature of the current adapter layer. For each knowledge-specific adapter, we concatenate the last hidden features of the pre-trained model and adapter as the final output feature of this adapter model.
In the pre-training procedure, we train each knowledge-specific adapter on different pre-training tasks individually. For various downstream tasks, K-ADAPTER can adopt the fine-tuning procedure similar to RoBERTa and BERT. When only one knowledge-speciffe adapter is adopted, we can take the final output feature of this adapter model as the input for task-specific layers of the downstream adapters are adopted, we concatenate the output features of
task. When multiple knowledge-spedee different adapter models as the input for task-specific layers of the downstream task.
3.2 PRE-TRAINING SETTINGS
We use RoBERTa,,ARGR (L=24, H=1024, A=16, 355M params) implementation by Huggingfacd as the pre-trained model in all our experiments. As for each adapter layer, we denote the number of transformer layer as N, the hidden dimension of transformer layer as HA. the number of self- attention heads as AA, the hidden dimension of down-projection and up up-projection layers as Ha and H,. In detail, we have the following adapter size: N 2, HA = 768, A = 12, H₂ = 1024 and H. 768. The RoBERTa layers where adapter layers plug in are (0.11,23), and different adapter layers do not share parameters. Thus the total parameters for each adapter model are about 42M, which are much smaller than RoBERTa and make the training process memory efffcient. should be noticed that RoBERTa is fixed during training and the parameters of adapters are trainable and initialized randomly. Then we describe how to inject different knowledge into knowledge- specific adapters as below.
3.3 FACTUAL ADAPTER
Factual knowledge can be described as the basic information that is concerned with facts. In this work, we acquire factual knowledge from the relationships among entities natural language. We extract a sub-dataset T-REx-re from T-REx (ElSahar et al. 2018) which is a large scale alignment dataset between Wikipedia abstracts and Wikidata triples. We discard all relations having less than 50 entity pairs, collecting 430 relations and 5.5M sentences. In order to inject factual knowledge, we propose to pre-train a mnowledge-specifue adapter called facAdapter on the relation classification task. This task requires model to classify relation labels of given entity pairs based context. Specifically, the last hidden features of RoBERTa and facAdapter are concatenated as the input representation, and the pooling layer is applied to the input representations of the given entities. Then, we concatenate two entity representations to perform relation classification.
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Table 2: Results on two entity typing datasets OpenEntity and FIGER.
OpenEntity
FIGER
Model
P
Mi-F;
Acc
Ma-Fi
Mi-F;
NEGEC Shimaoka et al. 2016
68.80
53.30
60.10
55.60
75.15
71.73
BERT-base (Zhang
2019)
76.37
70.96
73.56
52.04
75.16
71.63
ERNIE Zhang et al. 2019
78.42
72.90
75.56
57.19
75.61
73.39
KnowBERT (Ptters el al. 2019
78.60
73.70
76.10
KEPLER (Wang al 2019)
77.20
74.20
75.70
WKLM (Xion 2020
60.21
81.99
77.00
RoBERTa
77.55
74.95
76.23
56.31
82.43
77.83
RoBERTa multitask
77.96
76.00
76.97
59.86
84.45
78.84
K-ADAPTER (F+L)
78.99
76.27
77.61
61.81
84.87
80.54
K- ADAPTER (F)
79.30
75.84
77.53
59.50
84.52
80.42
K ADAPTER (L)
80.01
74.00
76.89
61.10
83.61
79.18
K X-ADAPTER (w/o knowledge)
74.47
74.91
76.17
56.93
82.56
77.90
3.4 LINGUISTIC ADAPTER
Linguistic knowledge is implicitly contained in natural language texts, e.g.. syntactic and seman- tic information. In this work, we acquire linguistic knowledge from dependency relationships among words in natural language text. We build dataset consisting of 1M examples. In partic- ular, we run the off-the-shell dependency parser from Stanford Parse on part of Book Corpus (Zhu al. 2.15p. To inject linguistic knowledge, we pre-train another knowledge-specifie adapter called Ainter on the task of dependency relation prediction. This task aims to predict the head index of each token the given sentence. We concatenate the last hidden features of RoBERTa and lin AinAdapter as the input representation, and then apply a linear layer to input representations of each token perform classification. More training details of facAdapter and linAde can be found in the supplementary material.
4 EXPERIMENTS
We evaluate our K-ADAPTER on three knowledge-driven downstream tasks, i.e., entity typing, ques- tion answering and relation classification. Furthermore, we conduct probing experiments to exam- ine the ability of models for learning factual knowledge. The notations of K-ADAPTER (F+L), K-ADAPTER (F), and K-ADAPTER (L) denote our model which consists of both factual adapter and linguistic adapter, only factual adapter and only linguistic adapter, respectively. The implementation details, and statistics of datasets are in the supplementary material.
4.1 ENTITY TYPING
We conduct experiments fine-grained entity typing which aims predict the types given entity and its context. We evaluate our models on OpenEntity (Choi et al. 2018) and FIGER (Ling 2013) following the same split setting as Zhang al. (2019) To fine-tune our models for entity typing, we modify the input token sequence by adding the special token "@" before and after a certain entity, then the first "@" special token representation is adopted to perform elassification. As for OpenEntity, we adopt micro F1 score the final metric to represent the model performance. As for FIGER, we adopt strict accuracy, loose macro, loose micro Fi scores (Ling & Weld 2012) for evaluation following the same evaluation criteria used in previous works.
Baselines NFGEC (Shimaoka al. 2016) employs attentive recursive neural networks com- pose context representations. KEPLER (Wang et al., 2019) integrates factual knowledge with the supervision of the knowledge embedding objective. KoBERTa-multitan pre-trained with multi-task learning (as shown in Figure aa)) for injecting multiple kinds of knowl- edge on two pre-training tasks. K-ADAPTER (w/o knowledge) consists RoBERTa model and adapter without being injected knowledge. Other baseline models, such BERT-base, ERNIE, KnowBERT and WKLM are described in Section
is our RoBERTa model
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Table 3: Results on question answering datasets including: CosmosQA, SearchQA and Quasar-T.
SearchQA
Quasar-T
CosmosQA
Model
EM
Fi
EM
Fi
Accuracy
BiDAF Seo et al.
2016)
28.60
34.60
25.90
28.50
AQA (Buck
2018
40.50
47.40
R33 (Wang el al.
2017a
49.00
55.30
35.30
41.70
DSQA (Lin el al.
2018)
49.00
55.30
42.30
49.30
Evidence Agg. Wang et al.
2018
$7.00
63,20
42.30
49,60
BERT (Xiong all (0200
57.10
61.90
40.40
46.10
WKLM (Xion al. 2020)
58.70
63.30
43.70
49,90
WKLM Ranking (Xiong et al.
2020
61.70
66.70
45.80
52.20
BERT- SART-FTRACe+.VV sw AG (Huang et al.
2019
68.70
RoBERTa
59.01
65.62
40.83
48.84
80.59
RoBERTa + multitask
59.92
66.67
44.62
51.17
81.19
K-ADAPTER (F+L)
61.96
67.31
46.32
53.00
81.83
K K-ADAPTER (F)
61.85
67.17
46.20
52.86
80.93
K-ADAPTER (L)
61.15
66.82
45.66
52.39
80.76
The results OpenEntity and FIGER shown in Table K- K-ADAPTER (F+L) achieves consistent improvements across these datasets. As for OpenEntity, our RoBERTa achieve better results than other baseline models. K-ADAPTER (F+L) further achieves improve- ment of 1.38% F1 over RoBERTa, which means factual knowledge and linguistic knowledge help to predict the types more accurately. As for FIGER, covers more entity types, and is more fine- grained than OpenEntity. Compared with WKLM, K-ADAPTER (F+L) improves the macro F1 by 2.88%, micro F1 by 2.54% and accuracy by 1.60%. This demonstrates that K-ADAPTER (F+L. benefits fine-grained entity typing. In addition, we further conduct several experiments our ab- lated model K-ADAPTER (w/o knowledge), to explore whether the performance gains came from introducing knowledge or additional parameters. Results show that K-ADAPTER (F) significantly outperforms K-ADAPTER (w/o knowledge). Moreover, is worth noting that on OpenEntity dataset, K-ADAPTER (w/o knowledge) even performs slightly worse than RoBERTa. These results demon- strate that our model gains improvement from knowledge instead of more parameters. Thus, for simplicity, don't discuss K-ADAPTER (w/o knowledge) in the following experiments.
Results and Discussion
4.2 QUESTION ANSWERING
We conduct experiments on two question answering (QA) tasks, i.e., commonsense QA and open- domain QA. Commonsense QA aims to answer questions with commonsense. We adopt CosmosQA (Hang et al. 2019) to evaluate prehension, formulated as multiple-choice questions. To fine- tune our models for CosmosQA, the /tspe/eeiiiooo////==s///eeeeeee
models. CosmosQA requires commonsense-
reading com-
input token sequence is modified " "<SEP>contexl
then the representation of the first token is adopted to perform classification, and will get score for this answer. After getting four scores, the answer with the highest score will be selected. We report accuracy scores obtained from the leaderboard.
Open-domain QA aims answer questions using external resources such as collections of docu- ments and webpages. We evaluate our modes on two public datasets, i.e., Quasar-T (Dhingra et al., 2017 and SearchQA (Dunn et al., 2017). Specifically, we first retrieve paragraphs correspond- ing to the question using the information retrieval system and then extract the answer from these retrieved paragraphs through the reading comprehension technique. Following previous workgLin et al. 2018), we use the retrieved paragraphs provided by Wang et al. (201bb) for these two datasets. To fine-tune our models for this task, the input token sequence We apply linear layers over the last hidden features of our model to predict the start and end position of the answer span. We adopt two metrics including ExactMatch (EM) and loose F1 Ling & Weld 2012) scores evaluate our models.
modified as "=SEP>qqestion
S/SEP>parrgraph><SSEP
Baselines BERT-FT RACE-SS AG (Huang et al., 2019 is the BERT model sequentially fine- tuned on both RACE and SWAG datasets. BiDAF (Seo et al. 2016) adopts bi-directional attention network. AQA (Buck et al. 2018) proposes to re-write questions and aggregate the answers gen- erated by the re-written questions. R'3 (Wang et al., 2017a) is reinforced model making of

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ranker for selecting most confident paragraph. Evidence Agg. (Wang et al., 2018) proposes making use of the aggregated evidence from across multiple paragraphs to better determine the answer with re-rankers. BERT (Xiong et al. 2020 is the BERT re- e--ipllementatio.. WKLM (Xiong et al., 2020) is described in Section 2 which is adopted as the reader model to read multiple paragraphs to predict single answer. WKLM M + Ranking (Xiong al., 2020) is WKLM paragraph reader plus with BERT based paragraph ranker to assign each paragraph a relevance score.
Results and Discussion The results on CosmosQA are shown in Table E Compared with BERT-FT BERT-FTRACE+ +.AE+SSWAG. AG. our RoBERTa significantly achieves 11.89% improvement of accuracy. Compared to RoBERTa, K-ADAPTER (F+L) further improves the accuracy by 1.24%, which indi- cates that K-ADAPTER can obtain better commonsense inference ability. Moreover, the performance of ablated K-ADAPTER models, i.e., K-ADAPTER (F) and K-ADAPTER (L) are clearly better than RoBERTa, but slightly lose compared with KoBERTa+mulltiaskk It is notable that K-ADAPTER (F+L) makes obvious improvement comparing with RoBERTa-multizak. This demonstrates that the combination of multiple knowledge-specifie adapters could achieve better performance.
The results for open-domain QA are shown in Table S K-ADAPTER models achieve better results
compared other baselines. This indicates that K-ADAPTER models make full use of the infused knowledge and accordingly benefit understanding the retrieved paragraphs to answer the question. Specifically, SearchQA, K-ADAPTER (F+L) makes significant improvement of 4.01 % F1 scores, comparing with WKLM where the ranking scores are not used, and even has slight improvement as compared WKLM+Ranking. is worth noting that K-ADAPTER models do consider the confidence of each retrieved paragraph, while WKLM+Ranking utilizes ranking scores from a BERT based ranker. On the Quasar-T dataset, K-ADAPTER (F+L) also outperforms WKLM by 3.1% F1 score and slightly outperforms WKLM+Ranking.
4.3 RELATION CLASSIFICATION
Relation classification aims to determine the correct relation between two entities in a given sen- tence. We adopt large-scale relation classification dataset TACRED (hang et al. 2017) T. fine- tune our models for this task, we modify the input token sequence by adding special token "@" before and after the first entity, adding "#" before and after the second entity. Then the token repre- sentations of the first special token "@" and "#" are concatenated to perform relation classification. We adopt micro Fi score as the metric to represent the model performance as previous works.
Baselines C-GCN Zhang et al., 2018) employs graph convolutional networks model de- pendency trees. BERT-large (Baldini Soares et al., 2019) is a baseline BERT-large model. BERT+MTB (Baldini Soares et al.) 2019) is method of training relation representation with- out supervision from a knowledge base by matching the blanks. Other baseline models, such as ERNIE, KnowBERT, KEPLER and RoBERTa+mulitask are described in Section and 4.1
Results and Discussion Table shows the performances of differ- ent models on TACRED. The sults indicate that K-ADAPTER mod- els significantly outperform all base- lines, which directly demonstrate our models benefit relation classifica- tion. In particular, (1) K-ADAPTER models outperform RoBERTa, which proves the effectiveness of infus- ing knowledge into pre-trained model with adapters. (2) K- K-ADAPTER mod- els gain more improvement compared This di- rectly demonstrates injecting knowl- edge individually in K-ADAPTER way would help models make full use of knowledge.
Table 4: Results on the relation classification dataset TA- CRED.
Model
P
R
F;
C-GCN Zhang et al. 2018
69.90
63.30
66.40
BERT-base (hhng ct al, 2019
67.23
64.81
66.00
ERNIE ZZang et al. 2019)
69.97
66.08
67.97
large (Baldin Soares et al. BERT+MTB (Baldmm Soares al
2019
70.10
2019
71.50
KnowBERT (Peters
2019
71.60
71.40
71.50
KEPLER (Wang al. 2019
70.43
73.02
71.70
RoBERTa
70.17
72.36
71.25
with RoBERTaamultitas
RoBERTa multitask
70.18
73.11
71.62
K- K-ADAPTER (F+L)
70.14
74.04
72.04
K- ADAPTER (F)
69.39
74.59
71.89
K-ADAPTER (I.)
68.85
75.37
71.96

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4.4 PROBING EXPERIMENTS
Although K-ADAPTER models have shown superior performance on knowledge-driven downstream tasks, it does not directly provide insights into whether our models infuse richer factual knowledge. Thus we utilize a LAMA (LAnguage Model Analysis) probe (Petroni et al., 2019 to examine the ability to memorize factual knowledge. Specifically, the LAMA probing task under a zero-shot setting, which requires the language model to answer cloze-style questions about relational facts without fine-tuning, e.g., "Simon Bowman was born in [MASK]". The model needs predict distribution over limited vocabulary to replace [MASK]. We report mean precision at one (P@) 1) macro-averaged over relations.
Table 5: P@ on LAMA and LAMA-UHN across Google-RE and T-REx corpora.
Models
Corpus
ELMo
ELMo5.5B
TransformeXXI
BaRT-large
RoBERTaLARGE
K-APDATER
LAMA-Google-EE
2.2
3.1
1.8
12.1
4.8
7.0
SAMA-UHN-Googleer
2.3
2.7
1.3
6.5
2.5
3.7
LAMA-T-REX
0.2
0.3
19.5
33.9
27.1
29.1
LAMA-UHN-T-REX
0.2
0.2
12.6
26.2
20.1
23.0
Table 6: Examples of generation for ROBERTa,,AGE and K-ADAPTER. The last column reports the top ranked predicted tokens. Correct predictions are in bold.
Query
Answer
Model
Generation
The native language of Mammootty is [MASK].
RoBERTa
English, Tamil, Hindi, Sanskrit, Arabic, Chinese
Malayalam
K-ADAPTER
Malayalam, Tamil, Hindi, Mandarin, English
RoBERTa
win, play, score, lose, run, drink, fly, roll, wait fly, swim, sing, shoot, kill, go, fish, drink, die cancer, death, illness, blindness, paralysis
Ravens can [MASK].
fly
K-ADAPTER
Sometimes virus causes [MASK].
infection
RoBERTa
K-ADAPTER
cancer, illness, deuth, infection, disease
Sunshine Coast, British Columbia is located in [MASK].
RoBERTa
Florida, California, Texas, Hawaii, Mexico
Canada
K ADAPTER
Canada, Vancouver, Victoria, BC, Australia
iPod Touch is produced by [MASK].
RoBERTa
Apple, Samsung, Qualcomm, LG, Microsoft
Apple
K-ADAPTER
Apple, HTC, Samsung, Motorola, Intel
Settings We consider several language models including: ELMo Peters et al. 2018), ELMo5.5B (Peters al. 2018), Transformer-X XL (Dai et al. 2019). BERT LARGE and RoBERTaLA LARGE- We focus on LAMA-GoogleRE and LAMA-T-REX, which are aimed at factual knowledge. We also con- duct probe experiments LAMA-UHN (Poerner al. 2019) more "factual" subset of LAMA, by filtering out queries that are easy to answer from entity names alone. Different models have dif- ferent vocabulary sizes. To conduct more fair comparison experiment, we adopt the intersection of vocabularies and let every language model rank only tokens in this vocabulary following Petroni al. (20T)). For simplicity, we only compare K-APDATER (F) which infused with factual knowl- edge, with other baseline models.
Results and Discussion Results are shown in Table It is surprising that BERT LARGE performs better than RoBERTa
There is one possible reason: BERT uses a character-level BPE (Gage) 1994) vocabulary, while RoBERTa considers byte-level BPE vocabulary. This finding indicates that, although using bytes makes it possible to learn a subword vocabulary that can encode any text without introducing "unknown" tokens, might indirectly harm the model's ability to learn factual knowledge, e.g., some proper nouns may be divided into bytes. Thus in the following experiments, we do not take BERT into account.
K-ADAPTER outperforms other models (except for BERT) by a huge margin. As for LAMA, com- pared to ROBERTa,ARGE, K-ADAPTER obtains 2.2% and 1.2% P@ improvement across Google- RE and T-REx, respectively. Moreover, compared to RoBERTa ARGE K-ADAPTER still achieves better results on LAMA-UHN. The results demonstrate that K-ADAPTER captures richer factual and commonsense knowledge than RoBERTa. Furthermore, Table shows several examples for the generation of RoBERTa AGE and K- ADAPTER for LAMA queries. From these examples, we can find that the objects predicted by K-ADAPTER are more accurate.
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Under review as a conference paper at ICLR 2021
4.5
CASE STUDY
Table / gives a qualitative comparison example between K-ADAPTER and RoBERTa on rela- tion classification dataset TACRED. The results show that, in most cases, the wrongly predicted logit value of RoBERTa and the logit value of the true label are actually quite close. For exam- ple, given "New Fabris closed down June 16", RoBERTa predicts "no relation", but the label "city of birth" ranks in second place. If a model could correctly predict the relationship between "New Fabris" and "June 16", then needs know that "New Fabris" is a company. Thanks to the factual knowledge in K K-ADAPTER, it can help the model from predicting "no ino.relation" to predicting the correct category label.
Table 7: case study for K-ADAPTER and RoBERTa on relation classification dataset TACRED. Underlines and wavy lines highlight the subject entities and object entities respectively. We report the top 3 ranked predictions.
Troe tabel
Modell
Predicted label
Predicted legits
His Sormer suueen Mark Deslin the Ubiveniit Parmeslaaii was so-kzaler the ofher, kmmon as the Microwave Asssomog telesoope.
K-Ailppeer
["ccheebgrrimddal 'snundation" (foundad)
[12.67 9 58, 5.76]
schoolk.ueenddd
('uuycuuiun" "oundeel"" pi"]
[9.8, 6.54,5007 5.07)
been cuandy Vessueerr Briish
ciiicue
K-Adigter
('éties "countiles of residence'. selation]
(13.26336666
RoBERE.
('ssemtic (raeeiiic_ ofuneyy alerrtteramms
[7.14, 7.08, 683)
Viadimir adyrhemski of Ressi after the autfered a Took
K-Adkgieer
['cause death" Viiigin Van relution]
716,067765,..44
spu charpionstipp Heinola seuthom city Finland, on Saumly
camecyffulasth
RoBERTa
["to relution', "cause deuth
[6.32] 5.0.555)
You can't I have good seaion unless start well. said Boll Mae in, usdounder of Tepp, on Satunday.
founded by
K Adipter
("Iunndes.yy" "memberutt" "emplrpec.at")
[10.55 9332, 2.377
[ ('mundbrinn' "Younder,hy" 'ompleyruaf")
[10.01 8.50,885,,.00
K-Ashpter
[dlieolvdd 'mogclation" placc)
[12.94,
New Fdltii chosal
RottlKIb
[' ('mueelutinn' "dleolved": 'dneut.
[11,49,944 3.31
At Wide which is finshing round of 12,000 jub sans CHRl Exccuiive Angele Moollo woid in intouncing the Bank Ambrica takeorer las sgek that housing and mongaas scsumn being strandl never loos dince
K-Athpier
['dieubed"; Inngelation, 'iboccel ploel))
[11.66 6-42]
dissolved
Depresiion.
RoBERT,
of plaat' pdatiin))
6.88,6633)
CONCLUSION AND FUTURE WORK
In this paper, we propose flexible and simple approach, called K-ADAPTER, infuse knowledge into large pre-trained models. K-ADAPTER remains the original parameters of pre-trained mod- els unchanged and supports continual knowledge infusion, i.e., kinds of injected-knowledge will not affect the parameters learned for old knowledge. Specifically, factual knowledge and lin- guistic knowledge are infused into RoBERTa with two kinds of adapters, which are pre-trained on the relation classification task and dependency relation prediction task, respectively. Extensive ex- periments on three knowledge adapter achieves significant improvement individually, and even more together. Probing experi- ments further suggest that K-ADAPTER captures richer factual and commonsense knowledge than RoBERTa. In future work, we will infuse more types of knowledge, and apply our framework to more pre-traee mre-trained models.
downstream tasks demonstrate that the performance of each
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Reviews:
[{"review_id": "sv5rbVdpwk", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The paper proposes a new approach to inject knowledge into pre-trained language representation models (PLMs). Instead of tuning the original PLM parameters, the paper plugs in new adapters for knowledge injection to avoid catastrophic forgetting. \n\nPros:\n* Injecting knowledge into PLMs is an advanced topic. The authors focus on the catastrophic forgetting problem during knowledge injection. \n* Evaluation is solid. The authors evaluate their model on three downstream tasks and show that the adapters improve the performance.\n* The paper is well written and can be easily understood. \n\nCons:\n* The approach is simple but achieves good performance over a variety of tasks. I appreciate that the authors conduct the knowledge probing experiment but its P@1 is quite low and worse than BERT. Some more explanations are expected.\n", "extra_scores": {}, "extra_text": {}}, {"review_id": "oJYENq2UhyT", "reviewer": "AnonReviewer4", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "##########################################################################\nReasons for score: \n\nThe authors propose a plug-in based adapter approach to allow for task specific parameter settings without updating the original pre-trained model which prevents the potential for catastrophic forgetting while also removing the need for separate models for separate tasks.  The work seems to build off Houlsby 19  as briefly cited, but its in plug-in nature seems easier to adopt for multiple tasks.  There is however not direct comparison with it or Cooper et al 19 ( https://arxiv.org/pdf/1902.02671.pdf ) which makes it difficult to assess.  The way in which the adaptors were pretrained was a little unclear to me.  The experiments are extensive and well done.\n\n \n##########################################################################\nPros:\n1) The number of experiments run ( 3 tasks on 6 datasets total ) are extensive and shows the K-adaptor approach can benefit from the factual adaptor in particular in giving better performance over RoBERTa ( with or without multi-task learning ).\n\n2) The proposed adaptor seems concise and easily expanded to incorporate other knowledge sources ( though there are few details which could help clarify things see #2 in next section )\n\n3) The probing task using LAMA to show how much factual knowledge has been memorized by the K Adaptor ( RoBERTA + facAdapter ) was well done and its discussion was very interesting. \n \n##########################################################################\nCons: \n1)  The proposed adapter solution is somewhat similar in nature to that of Houlsby 19  ( and to a lesser extent Cooper 19 ( https://arxiv.org/pdf/1902.02671.pdf ) ) and it feels like an omission to not discuss Houlsby 19 and make experimental comparisons against it discussing pros/cons more thoroughly especially since in the extensive experiments done in this work it is shown the linguistic adapter usually only adds a tenth of a percentage point when using RoBERTa with a single Factual adapter.  In this single adapter case then its not immediately evident how these models would differ and what the advantage is.   Both Houlsby and Cooper are evaluated on the GLUE benchmark and provide code.   \n\n2) I was a little confused as to how the adapters were specifically pre-trained and it might be a question of Figure 1b, but also sections 3.3 and 3.4 could have been expanded to clarify it a bit.  It is my understanding that when pre-training the facAdapter on the relation classification task for instance in Section 3.3, for a given example in T-REx-rc,  two entities and context are passed into RoBERTA whose weights remain fixed while those of the KIA units of the facAdapter are updated and the final hidden representations of RoBERTA and the facAdapter are concatenated to form an input representation of the entities given their context and this used for the actual task.  Is my understanding correct?  If so I'm confused as to how the subsequent pooling and concatenation actions are done.   Clarifying this process for 3.3 and 3.4 would be beneficial for clarity purposes and its not discussed in the supplemental materials either.\n\n3) Your RoBERTa Large baseline already beats most of what you are comparing against which is fine as your adapters give gains ( again particularly the facAdapter), but it also would have been interesting to see what sort of gains would have been achieved using a different less powerful model as the base RoBERTa small or just plain BERT and additionally some sort of ablation testing or explanation on the choices made for the adapter networks themselves ( ie, N=2 Transformers etc , hidden layer size, etc ) though its possible this could be left for future work.  For clarity in Figure 2 where you show N x Transformer Layer ( and N=2), I'm assuming the first Transformer Layer feeds directly into the second Transformer Layer which then feeds into the Up Projection layer correct?  If so it might be better just to show two transformer layers like that instead and additionally, naming the projection layers Up and Down Projection Layer respectively.\n\n##########################################################################\n\nQuestions during rebuttal period: \n\nPlease address and clarify the cons above \n \n#########################################################################\nSmall typos:\nIn Abstract:  we propose K-ADAPTER, which remains the original parameters  .... \"remains\"  ->  \"keeps\" or \"retains\"\n\nIn Introduction: they fail to continual learning   .....  \"fail at continual learning\"\n                              It remains the original representation    ....   \"remains\"  ->  \"leaves\"\n                              (pg2) while remaining the original parameters of RoBERTa frozen...  \"remaining\"  ->   \"keeping\"\n\nSection 3:     It remains the original representation    ....   \"remains\"  ->  \"keeps\"\n              3.1:  Different from Houlsby et al. (2019) add adapter layers  ->  \"In contrast to Houlsby et al. (2019) who add adapter layers\"\n              3.3:  all relations having lees than .... \"lees\" -> \"less\"\n\n", "extra_scores": {}, "extra_text": {}}, {"review_id": "WsrwLAqK-Y6", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "Summary:\nThe paper proposes a novel approach of incorporating different types of world knowledge sources contained in texts such as facts or linguistic syntax. To do this, they introduce additional transformers layers between the layers of a pre-trained language model such as Roberta and term this model as \"K-Adapters\", where the K stands for K different streams of knowledge.\n\nPros: \n- Incorporating different sources of information into a pre-trained model such as Roberta is an interesting idea. \n- The proposed approach is simple and interesting and it scales to many different types of information as the different adapters can be trained in parallel with the weights of the pre-trained LM being fixed.\n- Performance gains on different classification tasks such as entity tying, question-answering, and relation classification highlights the utility of the approach.\n\nCons:\n- Sec 1: Introduction: In the introduction, there are multiple mentions of the phrase “rich knowledge” but it is unclear what do the authors mean by that in the context of pre-trained language models. Some recent works such as “https://arxiv.org/abs/2002.08910, https://arxiv.org/abs/1909.01066 ” suggest that pretrained language models do indeed contain a lot of world factual knowledge. Hence, the statement in the paper that pertained LMs lack world knowledge is contradicting these works.\n- There is also a frequent mention of catastrophic forgetting of knowledge during the finetuning step. I tend to disagree that this is necessarily bad for a pretrained model, because it has been shown that finetuning pre-trained LMs perform well in open-domain question answering where some degree of world knowledge is needed. \n- Furthermore, producing entangled representations may not necessarily be a negative thing, if multi-task learning approaches are able to show an increase in performance due to knowledge injection.\n- In Table 1, dependency parser doesn't really fall under the same class of knowledge sources such as Wordnet or Wikidata. A dependency parser may be able to provide some sort of syntactic structure of the underlying text. Moreover, such syntactic information is not always generalizable to different domains and thus has the limitation of not being accurate enough.\n- The Introduction section is not well-motivated and does not present convincing arguments as to why external knowledge infusion is really required in some tasks. It just states that knowledge infusion using k-adapter model outperforms Roberta models in different tasks.\n- In Section 3.1, not enough space has been allocated to explain the Adapter model in detail. If the authors had used mathematical notation or equations for explanation, then it would have been much more clear. \n- In Section 4, it is mentioned that they select three downstream tasks for evaluating their models. However, the paper doesn't provide justifications as to why these tasks were selected, how can these tasks highlight the importance of k-adapter model, etc. \n- In the results table 2 and table 4, as the performance improvements are somewhat marginal, it is important to know if these improvements are statistically significant or not. The paper doesn't report if the results are from single run or the mean of multiple runs.\n- I have concerns about data leakage during the pre-training step. As the factual adapter makes use of supervised relation classification dataset (T-REx), I feel that there might be some overlap between entity typing and relation classification datasets used for evaluating the performance of the model. The authors should present an analysis as to what degree of overlap if any is present during the pre-training and evaluation tasks.\n- The paper lacks a detailed analysis section that could explain as to which test examples are getting correctly classified when using k-adapter model in tasks like relation classification, entity typing compared to other baseline approaches such as Roberta, Roberta + multitask. Currently, the paper pays just too much emphasis on raw numbers or performance improvements in various tasks.\n- The results of the probing experiments suggests that BERT-large model vastly outperforms k-adapter model on Google-Re and T-REx datasets probing datasets. This raises an important question over the validity of the results in different downstream tasks. For a fair comparison with baselines, the authors should compare the performance of the k-adapter model with BERT-large + multitask across different tasks.\n- In almost all of the experiments, the authors use Roberta as the underlying pre-trained language model. For demonstrating generalization to different pre-trained LMs, the paper should also evaluate when k-adapter model is trained when BERT-large or T5-large are used as underlying models in place of Roberta.\n\nGrammar errors: \n- page 1, 3rd line from bottom: remains -> retains\n- section 3.3, information that concerned -> information that is concerned\n- section 3.4, father index is commonly referred to as head index of a word.", "extra_scores": {}, "extra_text": {}}, {"review_id": "GsHAZBOZmnN", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 7, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "#### Summary\n\nThis submission proposes a general method (K-Adapter) for injecting knowledge (either factual or linguistic) into pre-trained language models.  The key architectural property of the approach is that K-Adapters are isolated from one another, allowing the use of multiple adapters without interference. These K-Adapter modules take hidden layer _inputs_ from the main pre-trained model (eg, BERT), and are pre-trained on their knowledge outputs before a fine-tuning phase where they feed into a joint downstream task-specific model along with the pre-trained model outputs.\n\n#### Strong and weak points\n\nThe set of baselines seem strong, and the experimental results consistently show that using _either_ factual or linguistic knowledge K-Adapters improves, while using _both_ yields the best results.\n\nThe LAMA probing experiment is a nice sanity or validation test that the knowledge injection is achieving the desired effect. Being able to \"hard-code\" knowledge into the model in this way could be useful in a variety of applications. It is overselling it a bit to say the model captures \"richer\" commonsense knowledge, however.\n\nThe basic architectural idea is well-motivated and simple, in a good way.\n\nThe supplemental materials mostly provide additional reproducibility details on architectures, hardware used, learning rates, etc.\n\n#### Recommendation (accept or reject) with one or two key reasons for this choice.\n\nI recommend to accept. The proposed approach yields strong quantitative performance against solid and relevant baselines, and the LAMA experiments give some support to the hypothesis that it is doing so by capturing knowledge as intended. The general design pattern could spur further innovations in modular network designs or knowledge capture strategies as well.\n\n#### Questions to clarify / additional evidence required\n\n\"BERT-MK inegrates fact triples from the knowledge graph.\" - how? I can follow the cite but this sentence provides little information.\n\n\"inject different types of knowledge independently\" - is it correct to say then, that, by design, there can be no _beneficial_ interactions or synergies among different types of knowledge? Alternatively, in the fine-tuning phase, could different adapters interact or affect each other via the downstream coupling in the task-specific layers? Is this observed in practice?\n\nHow should the reader think about the relative magnitude of the presented improvements? At one point I see \"K-ADAPTER (F+L) makes significant improvement of ...\" but I believe \"significance\" is only meant coloquially here. \n\nSection 3.1: how was this structure chosen, what was the motivation or intuition here?\n\nWhat limits, if any, do you foresee with the use of separate parallel \"knowledge modules\" like this? Could we use 10, 100, 1000 K-Adapters?\n\n#### Additional feedback to improve\n\nIt would be helpful to cite Ling and Weld 2012 (or similar) for the definition of \"loose\" micro/macro F1, or briefly explain it inline in the evaluation setup.  Likewise for the \"catastrophic forgetting\" phenomenon affecting other knowledge injection attempts - is there some previous work explicitly demonstrating this problem when using multiple knowledge sources? If not, it would have been interesting to have an experiment of this sort in this work.\n", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper augments pre-trained language models by introducing “adapter”, where each adapter is another language model pre-trained for a specific knowledge source (e.g., Wikidata) and an objective (e.g., relation classification). The representation from each adapter is concatenated to the representation from the generic LM. Specifically, they introduce two adaptors, “factual” (mostly derived from Wikipedia), and “linguistic” (from dependency parser), and the experiment shows modest improvements over various benchmarks.

This is a borderline paper, as both methods and experiments are reasonable yet not very novel or strong. The clarity of the paper can be improved (as pointed by R1 and R4), without any mathematical notations, model details are to be interpolated from figures. The novelty is limited and experimental rigor can be improved (i.e., for many settings, gains are fairly small and no variance reported). 
Decision: Reject